Machine learning methods for predicting disease phenotype

Abstract

The present disclosure provides a method for obtaining and using a gene set capable of classifying whether a patient has a first disease state or a second disease state. The method includes (a) providing a dataset as an input to a machine learning classifier, wherein said dataset comprises or is derived from gene expression measurements from a plurality of reference samples, of genes listed in a plurality of the gene modules; (b) performing feature selection to select a subset of gene modules from the plurality of gene modules; and (c) selecting differentially expressed genes from the genes listed in the subset of gene modules to obtain the gene set capable of classifying whether a patient has the first disease state, or the second disease state.

Description

MACHINE LEARNING METHODS FOR PREDICTING DISEASE PHENOTYPECROSS-REFERENCE

[0001] This application claims the benefit of priority to U.S. Provisional Application No. 63 / 539,063, filed on September 18, 2023, which is incorporated herein by reference in its entirety.BACKGROUND

[0002] Many diseases, including Systemic Uupus Erythematosus (SEE), are heterogeneous in nature, and have variable causation, course and responsiveness to therapy. There is a need for understanding biological pathways involved in the pathogenesis of these conditions to allow identification and optimization of therapies.SUMMARY

[0003] The present disclosure provides systems and methods for predicting a disease phenotype in a patient using machine learning as described herein. In some embodiments, the systems and methods comprise assaying gene expression from a patient sample to assess and / or classify the disease phenotype of a patient using machine learning. In some embodiments, systems and methods for predicting a disease phenotype in a patient using machine learning comprise classifying whether a patient has a first disease or a second disease as described herein.Certain Embodiments

[0004] The present disclosure provides systems and methods for predicting a disease phenotype in a patient using machine learning and uses thereof.

[0005] Provided herein are methods for obtaining a gene set capable of classifying whether a patient has a first disease state, or a second disease state, the methods comprising:(a) providing a dataset as an input to a machine learning classifier, wherein said dataset comprises or is derived from gene expression measurements from a plurality of reference samples of genes listed in a plurality of gene modules, wherein the plurality of reference samples comprises a first plurality of reference samples obtained or derived from reference subjects having the first disease state, and a second plurality of reference samples obtained or derived from reference subjects having the second disease state;(b) performing feature selection to select a subset of gene modules from the plurality of gene modules, wherein the plurality of gene modules forms the features of the machine learning classifier; and(c) selecting differentially expressed genes from the genes listed in the subset of gene modules to obtain the gene set capable of classifying whether a patient has the first disease state, or the second disease state.

[0006] In some embodiments, the machine learning classifier is sequential grouped feature importance (SGFI) algorithm.

[0007] In some embodiments, the feature selection comprises starting from a featureless model, and sequentially adding next best feature using leave-one-group-in importance (LOGI) until no further improvement in mean misclassification error (MMCE) over an improvement threshold is achieved.

[0008] In some embodiments, the improvement threshold is 0.00001, 0.00005, 0.0001, 0.0005, or 0.001.

[0009] In some embodiments, the dataset is a batch corrected dataset.

[0010] In some embodiments, the first disease state is having lupus and the second disease state is not having lupus.

[0011] In some embodiments, the first disease state is having inactive lupus and the second disease state is not having lupus.

[0012] In some embodiments, the first disease state is having active lupus and the second disease state is having inactive lupus.

[0013] In some embodiments, the first disease state is having lupus nephritis and the second disease state is not having lupus.

[0014] In some embodiments, the first disease state is having lupus nephritis and the second disease state is having non renal lupus.

[0015] In some embodiments, the first disease state is having lupus and the second disease state is having rheumatoid arthritis.

[0016] In some embodiments, the plurality of gene modules comprises gene modules listed in Table 1-1 to 1-46.

[0017] In some embodiments, the methods described herein, further comprising analyzing a patient data set comprising or derived from gene expression measurement of at least 2 genes selected from the genes listed in the gene set obtained in step (c) to classify whether the patient has the first disease state or the second disease state, wherein the gene expression measurement is obtained from a biological sample obtained or derived from the patient.

[0018] In some embodiments, the patient data set comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the gene set obtained in step (c).

[0019] In some embodiments, the patient data set comprises or is derived from gene expression measurements of the genes of the gene set obtained in step (c).

[0020] In some embodiments, the methods described herein, wherein the methods classify whether the patient has the first disease state or the second disease state with an accuracy of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0021] In some embodiments, the methods described herein, wherein the methods classify whether the patient has the first disease state or the second disease state with a sensitivity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0022] In some embodiments, the methods described herein, wherein the methods classify whether the patient has the first disease state or the second disease state with specificity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0023] In some embodiments, the methods described herein, wherein the methods classify whether the patient has the first disease state or the second disease state with a positive predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0024] In some embodiments, the methods described herein, wherein the methods classify whether the patient has the first disease state or the second disease state with a negative predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0025] In some embodiments, the methods described herein, wherein analyzing the patient data set comprises providing a patient data set as an input to a machine-learning model trained to generate an inference of indicative of whether the patient has the first disease state or the second disease state based on the patient data set.

[0026] In some embodiments, the methods described herein, further comprising: a) receiving, as an output of the machine-learning model, the inference; andb) electronically outputting a report classifying whether the patient has the first disease state or the second disease state based based on the inference.

[0027] In some embodiments, the machine -learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof.

[0028] In some embodiments, the machine-learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) of at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more than about 0.99, for classifying a disease state of the patient.

[0029] In some embodiments, the biological sample comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof.

[0030] In some embodiments, the methods described herein, further comprising selecting, recommending and / or administering a treatment to the patient based on a classification whether the patient has the first disease state or the second disease state.

[0031] Provided herein are methods for classifying whether a patient has lupus, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or more Tables selected from Table 1-7, Table 1-28 and Table 1-40 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus; receiving, as an output of the machine learning model the inference; and electronically outputting a report classifying whether the patient has lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0032] In some embodiments, the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-7, Table 1-28 and Table 1-40.

[0033] In some embodiments, Table 1-7 is selected.

[0034] In some embodiments, Table 1-7 and Table 1-28 are selected.

[0035] In some embodiments, Table 1-7, Table 1-28 and Table 1-40 are selected.

[0036] In some embodiments, the patient dataset comprising or derived from gene expression measurements of the genes listed in Table 1-7, Table 1-28 and Table 1-40.

[0037] Provided herein are methods for classifying whether a patient has inactive lupus or does not have lupus, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-1 and Table 1-7 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having inactive lupus or does not have lupus; receiving, as an output of the machine learning model the inference; and electronically outputting a report classifying whether the patient has inactive lupus or does not have lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0038] In some embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-1, and Table 1-7.

[0039] In some embodiments, Table 1-7 is selected.

[0040] In some embodiments, Table 1-1 and Table 1-7 are selected.

[0041] In some embodiments, the at least 2 genes selected comprises RSAD2, IFI6, HERC5, OAS3, IFI35, IFI27, IFI44, OASL, ISG15, IFI44L, IFIT1, IFIT3, OAS1, SAMD9L, HERC6, IFIT5, OAS2,IFITM1, EIF2AK2, SAMD9, GBP1, IFIT2, MX1, MX2, IFI30, GBP4, GBP2, CTLA4, SP110, ISG20, IFITM2, LAG3, SP1OO, CD160, KLRG1, HAVCR2, CD244, and ICOS.

[0042] Provided herein are methods for classifying whether a patient has active lupus or inactive lupus, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having active lupus or inactive lupus; receiving, as an output of the machine learning model the inference; and electronically outputting a report classifying whether the patient has active lupus or inactive lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0043] In some embodiments, the patient data set comprises gene expression measurements of the genes listed in Table 1-7.

[0044] Provided herein are methods for classifying whether a patient has lupus nephritis or does not have lupus, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7, and Table 1-28 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or does not have lupus; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or does not have lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0045] In some embodiments, the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-28.

[0046] In some embodiments, Table 1-7 is selected.

[0047] In some embodiments, Table 1-7 and Table 1-28 are selected.

[0048] In some embodiments, the at least 2 genes selected comprises IFI27, IFI44L, IFI44, IFITM1, OASL, RSAD2, SAMD9L, OAS3, IFI6, EIF2AK2, IFI35, HERC5, ISG15, GBP1, IFIT5, MX1, IFIT1, OAS2, LGALS3BP, SAMD9, HP, IFIT2, HERC6, MX2, IFIT3, MTF1, ASAP1, ISG20, GLS, SP110, IFITM2, IL1RN, GBP2, CD38, UBE2C, MARCKS, SPI1, OAS1, CASP1, B4GALT5, CDKN3, STAT4, BRCA1, SP100, TAP2, IFI30, SLAMF1, RELB, ARID3A, NFKBIA, FLNA, EGR1, NELL2, CXCL1, TNFAIP3, TYROBP, SOD2, ETAA1, MGLL, GP1BA, KMO, ACSL1, FCER2, ICAM1, TFF1, GMIP, PLAUR, INSIGI, G0S2, HHEX, IL1B, VEGFA, TNFAIP2, MAP3K4, SLC30A4, CTNND2, PIAS4, CCL20, GBP4, TAF15, NR3C1, EGR2, AK3, TBX3, CYP27B1, NFKB1, and CXCL3

[0049] Provided herein are methods for classifying whether a patient has lupus nephritis or non-renal lupus, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7 and Table 1-40 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or non-renal lupus; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or non-renal lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0050] In some embodiments, the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-40.

[0051] In some embodiments, Table 1-7 is selected.

[0052] In some embodiments, Table 1-7 and Table 1-40 are selected.

[0053] In some embodiments, the at least 2 genes selected comprises MRPL43, MRPL9, MRPL17, MRPL4, MRPL2, MRPL39, MRPL34, MRPL45, MRPL38, MRPL46, MRPL14, MRM1, MRPL30, MRPL13, MRPL3, HERC6, MRPL35, MRPL20, MRPL55, IFI35, MRPL42, MRPL18, MRPL50, OAS3, MRPL47, OAS1, IFI6, MRPL27, IFI44L, MRPL1, MRPL10, MRPL24, IFITM1, MRPL16, OAS2, MX1, MRPL33, IFI44, MX2, MRPL21, MRPL23, RSAD2, GBP4, MRPL44, IFIT3, ISG15, MRPL15, MRPL49, SAMD9L, IFIT1, IFI30, HERC5, SAMD9, MRPL51, EIF2AK2, IFIT5, MRPL37, OASL, SP100, MRPL32, MRPL11, MRPL48, MRPL53, MRPL19, MRPL41, MRPL52, MRPL28, IFI27, NSUN4, MRPL54, MRPL12, and IFIT2.

[0054] Provided herein are methods for classifying whether a patient has lupus or rheumatoid arthritis, the methods comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7, and Table 1-28 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus or rheumatoid arthritis; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus or rheumatoid arthritis based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

[0055] In some embodiments, the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-28.

[0056] In some embodiments, Table 1-7 is selected.

[0057] In some embodiments, Table 1-7 and Table 1-28 are selected.

[0058] In some embodiments, the at least 2 genes selected comprises HERC6, OASL, ISG15, IFI27, IFITM1, IFI6, OAS2, HERC5, IFI35, IFIT3, OAS1, RSAD2, OAS3, EIF2AK2, IFI44L, ISG20, LGALS3BP, IL1RN, MX1, MX2, IFIT1, IFI44, SAMD9L, IFIT5, SP100, IFIT2, RPGR, SP110, RELB, MRPS15, NFKB1, TAP2, CYP27B1, PATJ, GBP1, ITGA6, BRCA1, NFKB2, TYROBP, FLNA, GBP4, SAMD9, CASP1, GMIP, AKAP10, SPI1, CXCL8, GLS, SH3BP5, KLF1, PTGS2, VEGFA, AK3, GBP2, UBE2C, SDC4, AMPD3, ASAP1, MARCKS, ACLY, DAB2, IFI30, CASP10, IL1B, ETAA1, EREG, BHMT, NR3C1, MGLL, CD38, CD37, TFF1, INSIGI, TNFRSF11A, TSC22D1, PIAS4, TRAF1, CKB, HHEX, TBX3, EBI3, MAP3K4, MMP19, BHLHE41, INHBA, IFITM2, IL18, TNFAIP3, NFKBIA, HOMER2, APOL3, MSC, and EGR1.

[0059] In some embodiments, the patient dataset is derived from the gene expression measurements using gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene co-expression network analysis (MEGENA), weighted gene co-expression network analysis (WGCNA), differential expression analysis, Z-score, log2 expression analysis, or any combination thereof.

[0060] In some embodiments, the patient dataset is derived from the gene expression measurements using GSVA.

[0061] In some embodiments, a machine-learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof.

[0062] In some embodiments, the biological sample comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof.

[0063] In some embodiments, the methods described herein, further comprising selecting, recommending and / or administering a treatment to a patient based on an inference.

[0064] In some embodiments, the treatment is any treatment undergoing testing for, or approved for, treatment of an inferred disease state.BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The patent application file contains at least one drawing executed in color. Copies of this patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0066] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by referenceto the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0067] FIG. 1 : Flowchart to predict disease status using machine learning, according to one example of the disclosure. The datasets are quantile normalized, merged by common gene symbol, batch-corrected, and split into a train and test set. The scaled train set is inputted into SGFI. For each feature group combination chosen (n >= 5) in SGFI, a tuned radial SVM model is created, and 10-fold cross validation (CV) is performed. The model with the best Fl score is chosen as the final model and is evaluated on the test set.

[0068] FIGs. 2A-D: Results from the SLE vs. CTL pipeline. FIG. 2A. Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 2B) and after (FIG. 2C) batch effect correction with ComBat colored by dataset. FIG. 2D. Differential expression analysis results for the genes used in the final model, organized by gene set.

[0069] FIGs. 3A-C : Evaluation of the locked SLE vs. CTL model. A radial SVM model with the same hyperparameters and features as those chosen in the SLE vs. CTL pipeline was constructed using the train set and evaluated on the test set. FIG. 3A. The accuracy metrics from 10-fold cross validation on the train set and the evaluation of the model on the test set. The ROC (FIG. 3B) and PR (FIG. 3C) curves are shown.

[0070] FIGs. 4A-G: Results from the SLE vs. CTL pipeline. FIG. 4A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 4B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 4C) and PR (FIG. 4D) curves of the final model on the test set. GSVA results of IFN (FIG. 4E), Mito Large Ribo (FIG. 4F), and TNF (FIG. 4G) induced genes. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0071] FIGs. 5A-F: Results from the inactive SLE vs. CTL pipeline. FIG. 5A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 5B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 5C) and PR (FIG. 5D) curves of the final model on the test set. GSVA results of Anergic / Activated T cells (FIG. 5E) and IFN (FIG. 5F). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0072] FIGs. 6A-D: Results from the I-SLE vs. CTL pipeline. FIG. 6A. Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 6B) and after (FIG. 6C) batch effect correction with ComBat colored by dataset. FIG. 6D. Differential expression analysis results for the genes used in the final model, organized by gene set.

[0073] FIGs. 7A-D: Results from the active SLE vs. I-SLE pipeline. FIG. 7A. Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 7B) and after (FIG. 7C) batch effect correction with ComBat colored by dataset. FIG. 7D. Differential expression analysis results for the genes used in the final model, organized by gene set.

[0074] FIGs. 8A-E : Results from the active SLE vs. I-SLE pipeline. FIG. 8A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 8B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 8C) and PR (FIG. 8D) curves of the final model on the test set. GSVA results of IFN (FIG. 8E). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0075] FIGs. 9A-D: Results from the LN vs. CTL pipeline. FIG. 9A. Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 9B) and after (FIG. 9C) batch effect correction with ComBat colored by dataset. FIG. 9D. Differential expression analysis results for the genes used in the final model, organized by gene set.

[0076] FIGs. 10A-F : Results from the LN vs. CTL pipeline. FIG. 10A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 10B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 10C) and PR (FIG. 10D) curves of the final model on the test set. GSVA results of IFN (FIG. 10E) and TNF (FIG. 10F) induced genes. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0077] FIGs. 11A-D: Results from the LN vs. NRL pipeline. FIG. 11A. Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 11B) and after (FIG. 11C) batch effect correction with ComBat colored by dataset. FIG. 11D. Differential expression analysis results for the genes used in the final model, organized by gene set.

[0078] FIGs. 12A-F : Results from the LN vs. NRL pipeline. FIG. 12A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 12B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 12C) and PR (FIG. 12D) curves of the final model on the test set. GSVA results of IFN (FIG. 12E) and Mito Large Ribo (FIG. 12F). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0079] FIGs. 13A-E: Results from the LN vs. NRL pipeline using MCODE clusters as feature group definitions. FIG. 13A. MCODE clusters from protein-protein interactions, annotated with our curated gene sets. FIG. 13B. Sankey plot of the results from feature selection. Gene set combinations chosen infive or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’ . For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 13C. Results from evaluation of the final model on the test set. ROC (FIG. 13D) and PR (FIG. 13E) curves of final model on the test set.

[0080] FIGs. 14A-D: Results from the RA vs. SLE pipeline. FIG. 14A Gene symbols common across the different microarray platforms used in the pipeline. The PCA plots of the merged dataset before (FIG. 14B) and after (FIG. 14C) batch effect correction with ComBat colored by dataset. FIG. 14D.Differential expression analysis results for the genes used in the final model, organized by gene set.

[0081] FIGs. 15A-F: Results from the RA vs. SLE pipeline. FIG. 15A. Sankey plot of the results from feature selection. Gene set combinations chosen in five or more subsamples are shown in the plot. Gene sets are separated by the symbol, ‘+’. For each gene set combination, the MMCE, averaged across the subsamples in which that gene set combination was chosen, and the number of subsamples (n) is shown. FIG. 15B. Accuracy metrics from the evaluation of the final model on the test set. ROC (FIG. 15C) and PR (FIG. 15D) curves of the final model on the test set. GSVA results of IFN (FIG. 15E) and TNF (FIG.15F) induced genes. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.DETAILED DESCRIPTIONDefinitions

[0082] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0083] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0084] As used herein, the term “about” refers to an amount that is near the stated amount by 10%, 5%, or 1%, including increments therein.

[0085] As used herein, the phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[0086] As used herein, the term “Gini impurity” refers to a measure of how often a randomly chosen element from the set may be incorrectly labeled if it is randomly labeled according to the distribution of labels in the subset.

[0087] The use of the term “set” (e.g., “a set of items”) or “subset” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, the term “subset” of a corresponding set does not necessarily denote a proper subset of the corresponding set, but the subset and the corresponding set may be equal.

[0088] Reference in the specification to “embodiments,” “certain embodiments,” “preferred embodiments,” “specific embodiments,” “some embodiments,” “an embodiment,” “one embodiment” or “other embodiments” mean that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments, of the present disclosure.Methods

[0089] One aspect of the present disclosure is directed to a method for obtaining a gene set capable of classifying whether a patient has a first disease state or a second disease state. The method can include any one of, any combination of, or all of steps (a)-(c). Step (a) can include providing a dataset as an input to a machine learning classifier. The dataset can comprise or can be derived from gene expression measurements from a plurality of reference samples, of genes listed in a plurality of the gene modules. The plurality of reference samples can comprise a first plurality of reference samples obtained or derived from reference subjects having the first disease state, and a second plurality of reference samples obtained or derived from reference subjects having the second disease state. Step (b) can include performing feature selection to select a subset of gene modules from the plurality of gene modules. The plurality of the gene modules and / or genes within the modules can form the features of the machine learning classifier. The gene expression values can be the feature values. The features can be selected for the classification (e.g., separation) of the reference subjects having the first disease state, and the reference subjects having the second disease state. Step (c) can include selecting differentially expressed genes from the genes listed in the subset of gene modules to obtain the gene set capable of classifying whether a patient has the first disease state, or the second disease state. The differentially expressed genes are expressed differentially between the the reference subjects having the first disease state, and the reference subjects having the second disease state. The machine learning classifier (e.g., of step (a)) can be sequential grouped feature importance (SGFI) algorithm. The feature selection process (e.g., in step (b)) can include starting from a featureless model, sequentially adding next best feature using leave-one- group-in importance (LOGI) until no further improvement in mean misclassification error (MMCE) over an improvement threshold is achieved. The improvement threshold can be 0.00001, 0.00005, 0.0001, 0.0005, 0.001, or any value therebetween. In certain embodiments, the improvement threshold is 0.00001. In certain embodiments, the improvement threshold is 0.00005. In certain embodiments, the improvement threshold is 0.0001. In certain embodiments, the improvement threshold is 0.0005. In certain embodiments, the improvement threshold is 0.001. In certain embodiments, the dataset can be batch corrected dataset. In certain embodiments, the first disease state is having lupus and the second disease state is not having lupus. In certain embodiments, the first disease state is having inactive lupus and the second disease state is not having lupus. In certain embodiments, the first disease state is having active lupus and the second disease state is having inactive lupus. In certain embodiments, the first disease stateis having lupus nephritis and the second disease state is not having lupus. In certain embodiments, the first disease state is having lupus nephritis and the second disease state is having non renal lupus. In certain embodiments, the first disease state is having lupus and the second disease state is having rheumatoid arthritis. The plurality of the gene modules can be the gene modules listed in Table 1-1 to 1-46.

[0090] In certain embodiments, the method further comprises analyzing a patient dataset comprising or derived from gene expression measurement of at least 2 genes selected from the i) genes listed in the gene set obtained in step (c), or ii) genes listed in the subset of gene modules obtained in step (b), to classify whether the patient has the first disease state or the second disease state. In certain embodiments, the patient dataset comprising or derived from gene expression measurement of at least 2 genes selected from the genes listed in the gene set obtained in step (c). The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiment, the patient data set comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the gene set obtained in step (c). In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of the genes of the gene set obtained in step (c). In certain embodiment, the patient data set comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156,157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the subset of gene modules obtained in step (b). In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of the genes in the subset of gene modules obtained in step (b).

[0091] The method can classify whether the patient has the first disease state or the second disease with an accuracy of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a sensitivity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the seconddisease with specificity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a positive predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a negative predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0092] In certain embodiments, analyzing the patient data set comprises providing the patient data set as an input to a machine -learning model trained to generate an inference of indicative of whether the patient has the first disease state or the second disease state based on the patient data set. In certain embodiments, the method further comprises receiving, as an output of the machine -learning model, the inference; and / or electronically outputting a report classifying whether the patient has the first disease state or the second disease state based on the inference. The machine-learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof. The machine -learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) of at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more than about 0.99, for classifying whether the patient has the first disease state or the second disease state.

[0093] The biological sample can comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof.

[0094] In certain embodiments, the method further comprises selecting, recommending and / or administering a treatment to the patient based on the classification whether the patient has the first disease state or the second disease state. In certain embodiments, the method further comprises administering a treatment to the patient based on the classification whether the patient has the first disease state or the second disease state.

[0095] One aspect of the present disclosure is directed to a method for classifying a disease state of a patient. Classifying the disease state of the patient can include classifying i) whether the patient has lupus, ii) whether the patient has inactive lupus or does not have lupus, iii) whether the patient has active lupusor inactive lupus, iv) whether the patient has lupus nephritis or does not have lupus, v) whether the patient has lupus nephritis or non-renal lupus, or vi) whether the patient has lupus or rheumatoid arthritis.

[0096] Certain embodiments are directed to a method for classifying whether a patient has lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, Table 1-28 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, or any range therebetween genes selected from the genes listed in Table 1-7, Table 1-28 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7, Table 1-28 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-7, Table 1-28, and Table 1-40. In a non-limiting illustrative example, Table 1-7 and Table 1-28 are selected, thereby the patient data set comprises or is derived from gene expression measurement of i) at least 2 genes selected from the genes listed in Table 1-7, and ii) at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-7, Table 1-28, and Table 1-40. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-28 is selected. In certain embodiments, Table 1-40 is selected. In certain embodiments, Table 1-7 and Table 1-28 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1- 28. In certain embodiments, Table 1-7 and Table 1-40 are selected. In certain embodiments, Table 1-40 and Table 1-28 are selected. In certain embodiments, Table 1-7, Table 1-28, and Table 1-40 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, at least 2 genes selected from the genes listed in Table 1-28, and at least 2 genes selected from the geneslisted in Table 1-40. In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus; receiving, as an output of the machine-learning model the inference; and electronically outputting a report classifying whether the patient has lupus based on the inference. The method can classify whether the patient has lupus based on the inference. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for lupus. The treatment for lupus can be for treating lupus, reduce a severity of lupus, and / or reduce a risk of having lupus.

[0097] Certain embodiments are directed to a method for classifying whether a patient has inactive lupus or does not have lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-1, and Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, or any range therebetween genes selected from the genes listed in Table 1- 1, and Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-1 and Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of RSAD2, IFI6, HERC5, OAS3, IFI35, IFI27, IFI44, OASL, ISG15, IFI44L, IFIT1, IFIT3, OAS1, SAMD9L, HERC6, IFIT5, OAS2, IFITM1, EIF2AK2, SAMD9, GBP1, IFIT2, MX1, MX2, IFI30, GBP4, GBP2, CTLA4, SP110, ISG20, IFITM2, LAG3, SP100, CD160, KLRG1, HAVCR2, CD244, and ICOS. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MX2, EIF2AK2, OAS1, IFI44L, OASL, RSAD2, HERC6, IFIT5, IFI44, OAS3, SP100, IFI27, IFIT3, OAS2, HERC5, ISG15, SAMD9, MX1, IFIT1, IFIT2, IFITM1, GBP2, IFI35, PDCD1, KLRG1, GBP4, SAMD9L, CD160, ISG20, IFITM2, SP110, GBP1, IFI6, CD244, and HAVCR2. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or both Tables selected from Table 1-1, and Table 1-7. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from one or both Tables selected from Table 1-1, and Table 1-7. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-1 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-1. In certain embodiments, Table 1-1 and Table 1-7 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-1, and at least 2 genes selected from the genes listed in Table 1- 7. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having inactive lupus or does not have lupus; receiving, as an output of the machine-learning model the inference; and electronically outputting a report classifying whether the patient has inactive lupus or does not have lupus based on the inference. The method can classify whether the patient has inactive lupus or does not have lupus based on the inference. In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for inactive lupus. The treatment for inactive lupus can be for treating inactive lupus, reduce a severity of inactive lupus, and / or reduce a risk of having inactive lupus.

[0098] Certain embodiments are directed to a method for classifying whether a patient has active lupus or inactive lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or any range therebetween genes selected from the genes listed in Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MX2, EIF2AK2, IFI44L, OASL, OAS1, IFIT5, RSAD2, SP100, MX1, OAS2, IFI44, OAS3, HERC6, SAMD9, ISG15, IFIT2, IFIT3, IFI27, IFITM1, IFIT1, HERC5, GBP2, IFI35, SAMD9L, GBP4, IFITM2, SP110, and ISG20. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having active lupus or inactive lupus; receiving, as an output of the machine -learning model the inference; and electronically outputting a report classifying whether the patient has active lupus or inactive lupus based on the inference. The method can classify whether the patient has active lupus or inactive lupus based on the inference. In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for active lupus, e.g., when the inference is the patient data set is indicative of the patient having active lupus. In certain embodiments, the treatment is for inactive lupus,e.g., when the inference is the patient data set is indicative of the patient having inactive lupus. The treatment for active lupus can be for treating active lupus, reduce a severity of active lupus, and / or reduce a risk of having active lupus. The treatment for inactive lupus can be for treating inactive lupus, reduce a severity of inactive lupus, and / or reduce a risk of having inactive lupus.

[0099] Certain embodiments are directed to a method for classifying whether a patient has lupus nephritis or does not have lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,194, 195, 196, 197, 198, 199, 200 or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of B4GALT5, PLAUR, ISG20, IFIT2, TRAF1, VEGFA, BRCA1, SP100, STAT4, ACSL1, NFKBIA, BCL2A1, UBE2C, IFIT3, TNFRSF11A, IFI44, OASL, EIF2AK2, IFIT5, RSAD2, CASP10, CXCL1, IFIT1, CD38, MAP3K4, NELL2, HERC5, MX2, OAS3, SFRP1, MX1, SAMD9L, CKB, ARID3A, OAS1, FCER2, SLAMF1, PTGS2, NAMPT, INHBA, IFI44L, ISG15, CD83, IFI35, EGR2, SAMD9, IFI27, BIRC3, IFI6, MRPS15, LGALS3BP, RPGR, DAB2, SP110, TAP2, F3, MTF1, GBP2, CASP1, NFKB1, MARCKS, HP, CR2, GBP1, OAS2, ITGA6, CD37, NR3C1, EREG, and IFITM1. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of IFI27, IFI44L, IFI44, IFITM1, OASL, RSAD2, SAMD9L, OAS3, IFI6, EIF2AK2, IFI35, HERC5, ISG15, GBP1, IFIT5, MX1, IFIT1, OAS2, LGALS3BP, SAMD9, HP, IFIT2, HERC6, MX2, IFIT3, MTF1, ASAP1, ISG20, GLS, SP110, IFITM2, IL1RN, GBP2, CD38, UBE2C, MARCKS, SPI1, OAS1, CASP1, B4GALT5, CDKN3, STAT4, BRCA1, SP100, TAP2, IFI30, SLAMF1, RELB, ARID3A, NFKBIA, FLNA, EGR1, NELL2, CXCL1, TNFAIP3, TYROBP, SOD2, ETAA1, MGLL, GP1BA, KMO, ACSL1, FCER2, ICAM1, TFF1, GMIP, PLAUR, INSIGI, G0S2, HHEX, IL1B, VEGFA, TNFAIP2, MAP3K4, SLC30A4, CTNND2, PIAS4, CCL20, GBP4, TAF15, NR3C1, EGR2, AK3, TBX3, CYP27B1, NFKB1, and CXCL3. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of SLC30A4, TRAF1, IL18, GBP2, SP100, CD37, SLAMF1, RSAD2, FCER2, IFI27, PDE4DIP, BRCA1, GBP1, SAMD9L, EIF2AK2, CDKN3, SFRP1, CR2, CASP1, SP110, APOL3, HP, BIRC3, IFIT3, IFIT2, HERC5, INSIGI, TNFAIP3, EGR1, MAP3K4, IFI44L, IFI44, NELL2, STAT4,UBE2C, B4GALT5, TNF, ASAP1, INHBA, CASP10, IFIT1, KLF1, SAMD9, MX1, ACSL1, IFIT5, KMO, OASL, NFKBIA, ITGA6, OAS1, TAF15, VEGFA, IFITM1, OAS3, EPB41, CKB, TYROBP, CD83, ISG15, ETAA1, MSC, AMPD3, MMP19, CALD1, LGALS3BP, IFI35, EBB, IFI6, ISG20, BCL2A1, OAS2, CXCL2, ICAM1, MX2, HERC6, TBX3, KITLG, MGLL, GLS, NFKB1, AK3, and MTFE In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or both Tables selected from Table 1-7 and Table 1-28. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-28. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1- 28 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7 and Table 1-28 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1-28. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or does not have lupus; receiving, as an output of the machine -learning model the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or does not have lupus based on the inference. The method can classify whether the patient has lupus nephritis or does not have lupus based on the inference. In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for lupus nephritis. The treatment for lupus nephritis can be for treating lupus nephritis, reduce a severity of lupus nephritis, and / or reduce a risk of having lupus nephritis.

[0100] Certain embodiments are directed to a method for classifying whether a patient has lupus nephritis or non-renal lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MRPL43, MRPL9, MRPL17, MRPL4, MRPL2, MRPL39, MRPL34, MRPL45, MRPL38, MRPL46, MRPL14, MRM1, MRPL30, MRPL13, MRPL3, HERC6, MRPL35, MRPL20, MRPL55, IFI35, MRPL42, MRPL18, MRPL50, OAS3, MRPL47, OAS1, IFI6, MRPL27, IFI44L, MRPL1, MRPL10, MRPL24, IFITM1, MRPL16, OAS2, MX1, MRPL33, IFI44, MX2, MRPL21, MRPL23, RSAD2, GBP4, MRPL44, IFIT3, ISG15, MRPL15, MRPL49, SAMD9L, IFIT1, IFI30, HERC5, SAMD9, MRPL51, EIF2AK2, IFIT5, MRPL37, OASL, SP100, MRPL32, MRPL11, MRPL48, MRPL53, MRPL19, MRPL41, MRPL52, MRPL28, IFI27, NSUN4, MRPL54, MRPL12, and IFIT2. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MRPL33, MRPL3, MRPL22, MRPL48, MRPL18, MRPL20, MRPL45, and MRPL32. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or both Tables selected from Table 1-7, and Table 1- 40. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7, and Table 1-40. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-40 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-40. In certain embodiments, Table 1-7 and Table 1-40 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1- 4O.The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or non-renal lupus; receiving, as an output of the machinelearning model the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or non-renal lupus based on the inference. The method can classify whether the patient has lupus nephritis or non-renal lupus based on the inference. In certain embodiments, the method furthercomprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for lupus nephritis, e.g., when the inference is the patient data set is indicative of the patient having lupus nephritis. In certain embodiments, the treatment is for non-renal lupus, e.g., when the inference is the patient data set is indicative of the patient having non-renal lupus. The treatment for lupus nephritis can be for treating lupus nephritis, reduce a severity of lupus nephritis, and / or reduce a risk of having lupus nephritis. The treatment for non-renal lupus can be for treating non- renal lupus, reduce a severity of non-renal lupus, and / or reduce a risk of having non-renal lupus.

[0101] Certain embodiments are directed to a method for classifying whether a patient has lupus or rheumatoid arthritis. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,197, 198, 199, 200 or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of HERC6, OASL, ISG15, IFI27, IFITM1, IFI6, OAS2, HERC5, IFI35, IFIT3, OAS1, RSAD2, OAS3, EIF2AK2, IFI44L, ISG20, LGALS3BP, IL1RN, MX1, MX2, IFIT1, IFI44, SAMD9L, IFIT5, SP100, IFIT2, RPGR, SP110, RELB, MRPS15, NFKB1, TAP2, CYP27B1, PATJ, GBP1, ITGA6, BRCA1, NFKB2, TYROBP, FLNA, GBP4, SAMD9, CASP1, GMIP, AKAP10, SPI1, CXCL8, GLS, SH3BP5, KLF1, PTGS2, VEGFA, AK3, GBP2, UBE2C, SDC4, AMPD3, ASAP1, MARCKS, ACLY, DAB2, IFI30, CASP10, IL1B, ETAA1, EREG, BHMT, NR3C1, MGLL, CD38, CD37, TFF1, INSIGI, TNFRSF11A, TSC22D1, PIAS4, TRAF1, CKB, HHEX, TBX3, EBB, MAP3K4, MMP19, BHLHE41, INHBA, IFITM2, IL18, TNFAIP3, NFKBIA, HOMER2, APOL3, MSC, and EGR1. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of OAS2, OAS1, IFI27, MX1, EIF2AK2, IFI35, ISG15, IFI44L, IFI6, OAS3, OASL, HERC5, RSAD2, IFIT3, HERC6, SP100, IFI44, LGALS3BP, SAMD9L, IFIT1, MX2, IFITM1, IFIT2, IL1RN, SP110, IFIT5, GBP1, ISG20, TAP2, CASP1, GBP2, BRCA1, ITGA6, CASP10, SAMD9, GBP4, IFI30, CCL20, RPGR, PATJ, MTF1, UBE2C, KLF1, NFKB2, CD38, FLNA, SH3BP5, NR3C1, INHBA, PTGS2, AK3, HP, INSIGI, AMPD3, ASAP1, SLAMF1, VEGFA, SPI1, GLS, RELB, GMIP,ICAM1, AKAP10, TNFAIP3, TNFAIP2, SDC4, MRPS15, MGLL, IL16, ARID3A, STAT4, APOL3, G0S2, PLAUR, MMP19, CDKN3, EREG, EPB41, CR2, BHMT, NELL2, WT1, EGR1, ETAA1, MSC, FCER2, BIRC3, IFITM2, MAP3K4, TNFRSF11A, and NFKBIZ. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of LGALS3BP, HERC6, OAS1, IFI6, IFI35, ISG15, CR2, MX1, RELB, NFKB2, ISG20, HERC5, OAS3, IFI44L, RSAD2, OASL, SP100, IFIT3, OAS2, IFITM1, CD37, EIF2AK2, GBP4, IFI27, SP110, IFIT1, TAP2, SAMD9L, PTGS2, IFI44, MX2, CXCL8, TSC22D1, EREG, NFKB1, NAMPT, CD38, MSC, IFIT5, GBP1, CD83, IL1RN, GBP2, FCER2, NR3C1, SH3BP5, PATJ, MMP19, FLNA, MRPS15, and ITGA6. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of SP110, CD37, IFI27, OAS2, MX2, IFI35, OAS1, CXCL2, ISG15, GBP4, LGALS3BP, ETAA1, NAMPT, OASL, RSAD2, PTGS2, SP100, IFI44L, IFIT3, BCL2A1, MX1, OAS3, SAMD9L, NR3C1, HERC6, CD38, MMP19, and RPGR. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or both Tables selected from Table 1-7, and Table 1-28. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150 or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7, and Table 1-28. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1- 28 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7 and Table 1-28 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1-28. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiments, analyzing the patient dataset includes providing the patient dataset as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus or rheumatoid arthritis; receiving, as an output of the machine -learning model the inference; and electronically outputting a report classifying whether the patient has lupus or rheumatoid arthritis based on the inference. The method can classify whether the patient has lupus or rheumatoid arthritis based on the inference. In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for lupus, e.g., when the inference is the patient data set is indicative of the patient having lupus. In certain embodiments, the treatment is for rheumatoidarthritis, e.g., when the inference is the patient data set is indicative of the patient having rheumatoid arthritis. The treatment can be for lupus or rheumatoid arthritis. The treatment for lupus can be for treating lupus, reduce a severity of lupus, and / or reduce a risk of having lupus. The treatment for rheumatoid arthritis can be for treating rheumatoid arthritis, reduce a severity of rheumatoid arthritis, and / or reduce a risk of having rheumatoid arthritis.

[0102] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

[0103] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.I. Method for obtaining a gene set capable of classifying a disease state of a patient.

[0104] One aspect of the present disclosure is directed to a method for obtaining a gene set capable of classifying whether a patient has a first disease state, or a second disease state. The method can include any one of, any combination of, or all of steps (a)-(c). Step (a) can include providing a dataset as an input to a machine learning classifier. The dataset can comprise or can be derived from gene expression measurements from a plurality of reference samples, of genes listed in a plurality of the gene modules. The plurality of reference samples can comprises a first plurality of reference samples obtained or derived from reference subjects having the first disease state, and a second plurality of reference samples obtained or derived from reference subjects having the second disease state. Step (b) can include performing feature selection to select a subset of gene modules from the plurality of gene modules. The plurality of the gene modules and / or genes within the modules can form the features of the machine learning classifier. The gene expression values can be the feature values. The features can be selected for the classification (e.g., separation) of the reference subjects having the first disease state, and the reference subjects having the second disease state. Step (c) can include selecting differentially expressed genes from the genes listed in the subset of gene modules to obtain the gene set capable of classifying whether a patient has the first disease state, or the second disease state. The differentially expressed genes are expressed differentially between the the reference subjects having the first disease state, and the reference subjects having the second disease state. The machine learning classifier (e.g., of step (a)) can be sequential grouped feature importance (SGFI) algorithm. The feature selection process (e.g., in step (b)) can include starting from a featureless model, sequentially adding next best feature using leave-one- group-in importance (LOGI) until no further improvement in mean misclassification error (MMCE) over an improvement threshold is achieved. The improvement threshold can be 0.00001, 0.00005, 0.0001, 0.0005, or 0.001. In certain embodiments, the improvement threshold is 0.00001. In certain embodiments, the improvement threshold is 0.00005. In certain embodiments, the improvement threshold is 0.0001. In certain embodiments, the improvement threshold is 0.0005. In certain embodiments, the improvementthreshold is 0.001. In certain embodiments, the dataset can be batch corrected dataset. In certain embodiments, the first disease state is having lupus and the second disease state is not having lupus. In certain embodiments, the first disease state is having inactive lupus and the second disease state is not having lupus. In certain embodiments, the first disease state is having active lupus and the second disease state is having inactive lupus. In certain embodiments, the first disease state is having lupus nephritis and the second disease state is not having lupus. In certain embodiments, the first disease state is having lupus nephritis and the second disease state is having non renal lupus. In certain embodiments, the first disease state is having lupus and the second disease state is having rheumatoid arthritis. In certain embodiments, the plurality of the gene modules are the gene modules listed in Table 1-1 to 1-46. In certain embodiments, the plurality of gene modules are obtained by a method comprising: providing an initial data set comprising gene expression measurement from the plurality of the reference samples, of genes of an initial gene-set; selecting M genes from the initial gene set, said M genes are M variably expressed genes of the initial data set, wherein M is an integer; and clustering the M genes to obtain the plurality of the gene modules. In certain embodiments, the M genes are clustered based on protein protein interaction of the proteins encoded by the M genes. In certain embodiments, the M genes are M most variably expressed genes of the initial data set. As a non-limiting illustrative example, M is 2000 and M genes are the 2000 most variably expressed genes of the initial data set. In certain embodiments, M is 500 to 10000. In certain embodiments, M is 1000 to 8000. In certain embodiments, M is 1000. In certain embodiments, M is 2000. In certain embodiments, M is 3000. In certain embodiments, M is 4000. In certain embodiments, M is 5000. In certain embodiments, M is 6000. In certain embodiments, M is 7000. In certain embodiments, M is 8000.

[0105] In certain embodiments, the method further comprises analyzing a patient dataset comprising or derived from gene expression measurement of at least 2 genes selected from the i) genes listed in the gene set obtained in step (c), or ii) genes listed in the subset of gene modules obtained in step (b), to classify whether the patient has the first disease state or the second disease state. In certain embodiments, the patient dataset comprising or derived from gene expression measurement of at least 2 genes selected from the genes listed in the gene set obtained in step (c). The gene expression measurement can be obtained from a biological sample obtained or derived from the patient. In certain embodiment, the patient data set comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the gene set obtained in step (c). In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of the genes of the gene set obtained in step (c). In certain embodiments, the method comprises analyzing a patient dataset comprising or derived from geneexpression measurement of at least 2 genes selected from the genes listed in the subset of gene modules obtained in step (b) to classify whether the patient has the first disease state or the second disease state. In certain embodiment, the patient data set comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156,157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the subset of gene modules obtained in step (b). In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of the genes in the subset of gene modules obtained in step (b).

[0106] The method can classify whether the patient has the first disease state or the second disease with an accuracy of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a sensitivity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with specificity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a positive predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify whether the patient has the first disease state or the second disease with a negative predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0107] In certain embodiments, analyzing the patient data set comprises providing the patient data set as an input to a machine -learning model trained to generate an inference of indicative of whether the patient has the first disease state or the second disease state based on the patient data set. In certain embodiments, the method further comprises receiving, as an output of the machine -learning model, the inference; and / or electronically outputting a report classifying whether the patient has the first disease state or the second disease state based on the inference. The machine-learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vectormachine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof. The machine -learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) of at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more than about 0.99, for classifying whether the patient has the first disease state or the second disease state.

[0108] The biological sample can comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof. In certain embodiments, the biological sample comprises a blood sample or any derivative thereof. In certain embodiments, the biological sample comprises PBMCs or any derivative thereof. In certain embodiments, the biological sample comprises a tissue biopsy sample, such as kidney biopsy sample or any derivative thereof. The patient can be a human.

[0109] In some embodiments, the reference biological samples comprise blood samples, isolated peripheral blood mononuclear cells (PBMCs), tissue biopsy samples, nasal fluid samples, saliva samples, or any derivative thereof. In some embodiments, the reference biological samples comprise blood samples or any derivative thereof. In some embodiments, the reference biological samples comprise isolated peripheral blood mononuclear cells (PBMCs) or any derivative thereof. In some embodiments, the reference biological samples comprise tissue biopsy samples or any derivative thereof. Tissue can be skin tissue, or kidney tissue. The reference subjects can be human.

[0110] In certain embodiments, the method further comprises selecting, recommending and / or administering a treatment to the patient based on the classification whether the patient has the first disease state or the second disease state. In certain embodiments, the method further comprises administering a treatment to the patient based on the classification whether the patient has the first disease state or the second disease state.II. Method for classifying a disease state of a patient[oni] One aspect of the present disclosure is directed to a method for classifying a disease state of a patient. Classifying the disease state of the patient can include classifying i) whether the patient has lupus, ii) whether the patient has inactive lupus, iii) whether the patient has active lupus or inactive lupus, iv) whether the patient has lupus nephritis, v) whether the patient has lupus nephritis or non-renal lupus, or vi) whether the patient has lupus or rheumatoid arthritis.Ila. Method for classifying whether the patient has lupus.

[0112] Certain embodiments are directed to a method for classifying whether a patient has lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, Table 1-16, Table 1-28, Table 1-29, Table 1-40, and Table 1-46. In certain embodiments, the patient data set comprises or is derivedfrom gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188,189, 190, 200, 250, 300, 350, 400, or all, any range therebetween genes selected from the genes listed in Table 1-7, Table 1-16, Table 1-28, Table 1-29, Table 1-40, and Table 1-46. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,181, 182, 183, 184, 185, 186, 187, 188, 189, or any range therebetween genes selected from the genes listed in Table 1-7, Table 1-28, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7, Table 1-28 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-7, Table 1-16, Table 1-28, Table 1-29, Table 1-40, and Table 1-46. In a non-limiting illustrative example, Table 1-7 and Table 1-28 are selected, thereby the patient data set comprises or is derived from gene expression measurement of i) at least 2 genes selected from the genes listed in Table 1- 7, and ii) at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-7, Table 1-16, Table 1-28, Table 1-29, Table 1- 40, and Table 1-46. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-28 is selected. In certain embodiments, Table 1-40 is selected. In certain embodiments, Table 1-7 and Table 1-28 areselected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7 and Table 1-40 are selected. In certain embodiments, Table 1-40 and Table 1-28 are selected. In certain embodiments, Table 1-7, Table 1-28, and Table 1-40 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, at least 2 genes selected from the genes listed in Table 1-28, and at least 2 genes selected from the genes listed in Table 1-40. In certain embodiments, Table 1-7, Table 1-16, and Table 1-29 are selected. In certain embodiments, Table 1-7, Table 1-29, and Table 1-40 are selected. In certain embodiments, Table 1-7, and Table 1-16 are selected. In certain embodiments, Table 1-7, and Table 1-29 are selected. In certain embodiments, Table 1-16, and Table 1-29 are selected. In certain embodiments, Table 1-7, and Table 1-46 are selected. In certain embodiments, Table 1-16 is selected. In certain embodiments, Table 1-29 is selected. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0113] In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus; receiving, as an output of the machine-learning model the inference; and electronically outputting a report classifying whether the patient has lupus based on the inference. The method can classify whether the patient has lupus based on the inference. The machinelearning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having lupus, and ii) a second plurality of the reference biological samples obtained or derived from reference subjects not having lupus. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-7, Table 1-16, Table 1-28, Table 1-29, Table 1-40, and Table 1-46. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. The Tables selected, and genes selected from a selected Table for the patient data set and the reference data set can be the same, and can be as described (e.g., for the patient data set) herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that the trained machine learningmodel can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0114] In certain embodiments, the patient is at elevated risk of having lupus. In certain embodiments, the patient is suspected of having lupus. In certain embodiments, the patient is asymptomatic for lupus.

[0115] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for lupus. The treatment for lupus can be for treating lupus, reduce a severity of lupus, and / or reduce a risk of having lupus. lib. Method for classifying whether the patient has inactive lupus or does not have lupus

[0116] Certain embodiments are directed to a method for classifying whether a patient has inactive lupus or does not have lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-1, Table 1-7, Table 1-12, Table 1-19, Table 1-20, Table 1-21, and Table 1-39. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 200, 250, 300, 350, 400, or, all, or any range therebetween genes selected from the genes listed in Table 1-1, Table 1-7, Table 1-12, Table 1-19, Table 1-20, Table 1-21, and Table 1-39. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, or any range therebetween genes selected from the genes listed in Table 1-1, and Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-1 and Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of RSAD2, IFI6, HERC5, OAS3, IFI35, IFI27, IFI44, OASL, ISG15, IFI44L, IFIT1, IFIT3, OAS1, SAMD9L, HERC6, IFIT5, OAS2, IFITM1, EIF2AK2, SAMD9, GBP1, IFIT2, MX1, MX2, IFI30, GBP4, GBP2, CTLA4, SP110, ISG20, IFITM2, LAG3, SP100, CD160, KLRG1, HAVCR2, CD244, and ICOS. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MX2, EIF2AK2, OAS1, IFI44L, OASL, RSAD2, HERC6, IFIT5, IFI44, OAS3, SP100,IFI27, IFIT3, 0AS2, HERC5, ISG15, SAMD9, MX1, IFIT1, IFIT2, IFITM1, GBP2, IFI35, PDCD1, KLRG1, GBP4, SAMD9L, CD160, ISG20, IFITM2, SP110, GBP1, IFI6, CD244, and HAVCR2. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-1, Table 1-7, Table 1-12, Table 1-19, Table 1-20, Table 1-21, and Table 1-39. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-1, Table 1-7, Table 1-12, Table 1-19, Table 1-20, Table 1-21, and Table 1-39. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-1 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-1. In certain embodiments, Table 1-1 and Table 1-7 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-1, and at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-7 and Table 1-20 are selected. In certain embodiments, Table 1-7 and Table 1-39 are selected. In certain embodiments, Table 1-7 and Table 1- 21 are selected. In certain embodiments, Table 1-7 and Table 1-19 are selected. In certain embodiments, Table 1-7 and Table 1-12 are selected. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0117] In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having inactive lupus or does not have lupus; receiving, as an output of the machine-learning model the inference; and electronically outputting a report classifying whether the patient has inactive lupus or does not have lupus based on the inference. The method can classify whether the patient has inactive lupus or does not have lupus based on the inference. The machine -learning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having inactive lupus, and ii) a second plurality of the reference biological samples obtained or derived from reference subjects not having lupus. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-1, Table 1-7, Table 1-12, Table 1-19, Table 1-20, Table 1-21, and Table 1-39. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. The Tables selected, and genes selected from a selected Table for the patient data set and the reference data set can be the same, and can be as described (e.g., for the patient data set) herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that the trained machine learning model can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0118] In certain embodiments, the patient is at elevated risk of having lupus. In certain embodiments, the patient is suspected of having lupus. In certain embodiments, the patient is asymptomatic for lupus.

[0119] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for inactive lupus. The treatment for inactive lupus can be for treating inactive lupus, reduce a severity of inactive lupus, and / or reduce a risk of having inactive lupus. lie. Method for classifying whether the patient has active lupus or inactive lupus.

[0120] Certain embodiments are directed to a method for classifying whether a patient has active lupus or inactive lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-1, Table 1-3, Table 1- 7, Table 1-9, Table 1-22, and Table 1-44. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,146, 147, 148, 149, 150, 200, 250, 300, 350, 400, or all, or any range therebetween genes selected fromthe genes listed in Table 1-1, Table 1-3, Table 1-7, Table 1-9, Table 1-22, and Table 1-44. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or any range therebetween genes selected from the genes listed in Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MX2, EIF2AK2, IFI44L, OASL, OAS1, IFIT5, RSAD2, SP100, MX1, OAS2, IFI44, OAS3, HERC6, SAMD9, ISG15, IFIT2, IFIT3, IFI27, IFITM1, IFIT1, HERC5, GBP2, IFI35, SAMD9L, GBP4, IFITM2, SP110, and ISG20. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-1, Table 1-3, Table 1-7, Table 1-9, Table 1-22, and Table 1-44. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-1, Table 1-3, Table 1-7, Table 1-9, Table 1-22, and Table 1-44. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-7 and Table 1-9 are selected. In certain embodiments, Table 1-7 and Table 1-44 are selected. In certain embodiments, Table 1-7 and Table 1-3 are selected. In certain embodiments, Table 1-7 and Table 1-1 are selected. In certain embodiments, Table 1-7 and Table 1-22 are selected.

[0121] The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0122] In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having active lupus or inactive lupus; receiving, as an output of the machinelearning model the inference; and electronically outputting a report classifying whether the patient has active lupus or inactive lupus based on the inference. The method can classify whether the patient has active lupus or inactive lupus based on the inference. The machine-learning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having active lupus, and ii) a second plurality of the reference biological samples obtained or derived from referencesubjects having inactive lupus. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-7. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that the trained machine learning model can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0123] In certain embodiments, the patient is at elevated risk of having lupus. In certain embodiments, the patient is suspected of having lupus. In certain embodiments, the patient is asymptomatic for lupus. In certain embodiments, the patient has lupus.

[0124] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for active lupus, e.g., when the inference is the patient data set is indicative of the patient having active lupus. In certain embodiments, the treatment is for inactive lupus, e.g., when the inference is the patient data set is indicative of the patient having inactive lupus. The treatment for active lupus can be for treating active lupus, reduce a severity of active lupus, and / or reduce a risk of having active lupus. The treatment for inactive lupus can be for treating inactive lupus, reduce a severity of inactive lupus, and / or reduce a risk of having inactive lupus. lid. Method for classifying whether the patient has lupus nephritis or does not have lupus

[0125] Certain embodiments are directed to a method for classifying whether a patient has lupus nephritis or does not have lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-3, Table 1-7, Table 1-16, Table 1-18, Table 1-28 and Table 1-43. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 250, 300, 350, 400, or all or any range therebetween genes selected from the genes listed in Table 1-3, Table 1-7, Table 1-16, Table 1-18, Table 1-28, and Table 1-43. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of B4GALT5, PLAUR, ISG20, IFIT2, TRAF1, VEGFA, BRCA1, SP100, STAT4, ACSL1, NFKBIA, BCL2A1, UBE2C, IFIT3, TNFRSF11A, IFI44, OASL, EIF2AK2, IFIT5, RSAD2, CASP10, CXCL1, IFIT1, CD38, MAP3K4, NELL2, HERC5, MX2, OAS3, SFRP1, MX1, SAMD9L, CKB, ARID3A, OAS1, FCER2, SLAMF1, PTGS2, NAMPT, INHBA, IFI44L, ISG15, CD83, IFI35, EGR2, SAMD9, IFI27, BIRC3, IFI6, MRPS15, LGALS3BP, RPGR, DAB2, SP110, TAP2, F3, MTF1, GBP2, CASP1, NFKB1, MARCKS, HP, CR2, GBP1, OAS2, ITGA6, CD37, NR3C1, EREG, and IFITM1. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of IFI27, IFI44L, IFI44, IFITM1, OASL, RSAD2, SAMD9L, OAS3, IFI6, EIF2AK2, IFI35, HERC5, ISG15, GBP1, IFIT5, MX1, IFIT1, OAS2, LGALS3BP, SAMD9, HP, IFIT2, HERC6, MX2, IFIT3, MTF1, ASAP1, ISG20, GLS, SP110, IFITM2, IL1RN, GBP2, CD38, UBE2C, MARCKS, SPI1, OAS1, CASP1, B4GALT5, CDKN3, STAT4, BRCA1, SP100, TAP2, IFI30, SLAMF1, RELB, ARID3A, NFKBIA, FLNA, EGR1, NELL2, CXCL1, TNFAIP3, TYROBP, SOD2, ETAA1, MGLL, GP1BA, KMO, ACSL1, FCER2, ICAM1, TFF1, GMIP, PLAUR, INSIGI, G0S2, HHEX, IL1B, VEGFA, TNFAIP2, MAP3K4, SLC30A4, CTNND2, PIAS4, CCL20, GBP4, TAF15, NR3C1, EGR2, AK3, TBX3, CYP27B1, NFKB1, and CXCL3. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of SLC30A4, TRAF1, IL18, GBP2, SP100, CD37, SLAMF1, RSAD2, FCER2, IFI27, PDE4DIP, BRCA1, GBP1, SAMD9L, EIF2AK2, CDKN3, SFRP1, CR2,CASP1, SP110, APOL3, HP, BIRC3, IFIT3, IFIT2, HERC5, INSIGI, TNFAIP3, EGR1, MAP3K4, IFI44L, IFI44, NELL2, STAT4, UBE2C, B4GALT5, TNF, ASAP1, INHBA, CASP10, IFIT1, KLF1, SAMD9, MX1, ACSL1, IFIT5, KMO, OASL, NFKBIA, ITGA6, OAS1, TAF15, VEGFA, IFITM1, OAS3, EPB41, CKB, TYROBP, CD83, ISG15, ETAA1, MSC, AMPD3, MMP19, CALD1, LGALS3BP, IFI35, EBI3, IFI6, ISG20, BCL2A1, OAS2, CXCL2, ICAM1, MX2, HERC6, TBX3, KITLG, MGLL, GLS, NFKB1, AK3, and MTFE In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-3, Table 1-7, Table 1-16, Table 1-18, Table 1-28, and Table 1-43. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-3, Table 1-7, Table 1-16, Table 1-18, Table 1-28, and Table 1-43. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-28 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7 and Table 1-28 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-3 and Table 1-28 are selected. In certain embodiments, Table 1-16 and Table 1-43 are selected. In certain embodiments, Table 1-16 and Table 1-18 are selected. In certain embodiments, Table 1-16 is selected. In certain embodiments, Table 1-3 is selected. In certain embodiments, Table 1-18 is selected. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0126] In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or does not have lupus; receiving, as an output of the machine-learning model the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or does not have lupus based on the inference. The method can classify whether the patient has lupus nephritis or does not have lupus based on the inference. The machinelearning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having lupus nephritis, and ii) a second plurality of the reference biologicalsamples obtained or derived from reference subjects not having lupus. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-3, Table 1-7, Table 1-16, Table 1-18, Table 1-28, and Table 1-43. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. The Tables selected, and genes selected from a selected Table for the patient data set and the reference data set can be the same, and can be as described (e.g., for the patient data set) herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that the trained machine learning model can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0127] In certain embodiments, the patient is at elevated risk of having lupus. In certain embodiments, the patient is suspected of having lupus. In certain embodiments, the patient is asymptomatic for lupus. In certain embodiments, the patient has lupus. In certain embodiments, the patient is at elevated risk of having lupus nephritis. In certain embodiments, the patient is suspected of having lupus nephritis. In certain embodiments, the patient is asymptomatic for lupus nephritis.

[0128] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. The treatment can be for lupus nephritis. The treatment for lupus nephritis can be for treating lupus nephritis, reduce a severity of lupus nephritis, and / or reduce a risk of having lupus nephritis.He. Method for classifying whether the patient has lupus nephritis, or non-renal lupus

[0129] Certain embodiments are directed to a method for classifying whether a patient has lupus nephritis or non-renal lupus. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-1, Table 1-3, Table 1- 7, Table 1-13, Table 1-28, Table 1-29, Table 1-40, Table 1-41, and Table 1-46. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 200, 250, 300, 350, 400, or all, or any range therebetween genes selected from the genes listed in Table 1-1, Table 1-3, Table 1-7, Table 1-13, Table 1-28, Table 1-29, Table 1-40, Table 1-41, and Table 1-46. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MRPL43, MRPL9, MRPL17, MRPL4, MRPL2, MRPL39, MRPL34, MRPL45, MRPL38, MRPL46, MRPL14, MRM1, MRPL30, MRPL13, MRPL3, HERC6, MRPL35, MRPL20, MRPL55, IFI35, MRPL42, MRPL18, MRPL50, OAS3, MRPL47, OAS1, IFI6, MRPL27, IFI44L, MRPL1, MRPL10, MRPL24, IFITM1, MRPL16, OAS2, MX1, MRPL33, IFI44, MX2, MRPL21, MRPL23, RSAD2, GBP4, MRPL44, IFIT3, ISG15, MRPL15, MRPL49, SAMD9L, IFIT1, IFI30, HERC5, SAMD9, MRPL51, EIF2AK2, IFIT5, MRPL37, OASL, SP100, MRPL32, MRPL11, MRPL48, MRPL53, MRPL19, MRPL41, MRPL52, MRPL28, IFI27, NSUN4, MRPL54, MRPL12, and IFIT2. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of MRPL33, MRPL3, MRPL22, MRPL48, MRPL18, MRPL20, MRPL45, and MRPL32. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-1, Table 1-3, Table 1-7, Table 1-13, Table 1-28, Table 1-29, Table 1-40, Table 1-41, and Table 1-46. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-1, Table 1-3, Table 1-7, Table 1-13, Table 1-28,Table 1-29, Table 1-40, Table 1-41, and Table 1-46. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-40 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-40. In certain embodiments, Table 1-7 and Table 1-40 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, and at least 2 genes selected from the genes listed in Table 1-40. In certain embodiments, Table 1-7 and Table 1-3 are selected. In certain embodiments, Table 1-7 and Table 1-1 are selected. In certain embodiments, Table 1-41 is selected. In certain embodiments, Table 1-3 is selected. In certain embodiments, Table 1-46 is selected. In certain embodiments, Table 1-29 is selected. In certain embodiments, Table 1-28 is selected. In certain embodiments, Table 1-7 and Table 1-13 are selected. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0130] In certain embodiments, analyzing the patient data set includes providing the patient data set as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or non-renal lupus; receiving, as an output of the machinelearning model the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or non-renal lupus based on the inference. The method can classify whether the patient has lupus nephritis or non-renal lupus based on the inference. The machine-learning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having lupus nephritis, and ii) a second plurality of the reference biological samples obtained or derived from reference subjects having non-renal lupus. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-1, Table 1-3, Table 1-7, Table 1-13, Table 1-28, Table 1-29, Table 1-40, Table 1-41, and Table 1-46. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. The Tables selected, and genes selected from a selected Table for the patient data set and the reference data set can be the same, and can be as described (e.g., for the patient data set) herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that thetrained machine learning model can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0131] In certain embodiments, the patient is at elevated risk of having lupus. In certain embodiments, the patient is suspected of having lupus. In certain embodiments, the patient is asymptomatic for lupus. In certain embodiments, the patient has lupus.

[0132] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for lupus nephritis, e.g., when the inference is the patient data set is indicative of the patient having lupus nephritis. In certain embodiments, the treatment is for non-renal lupus, e.g., when the inference is the patient data set is indicative of the patient having non-renal lupus. The treatment for lupus nephritis can be for treating lupus nephritis, reduce a severity of lupus nephritis, and / or reduce a risk of having lupus nephritis. The treatment for non-renal lupus can be for treating non-renal lupus, reduce a severity of non-renal lupus, and / or reduce a risk of having non-renal lupus.Ilf. Method for classifying whether the patient has lupus or rheumatoid arthritis

[0133] Certain embodiments are directed to a method for classifying whether a patient has lupus or rheumatoid arthritis. The method can include analyzing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, Table 1-16, Table 1-22, Table 1-28, Table 1-34, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123,124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165,166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 250, 300, 350, 400, or all, or any range therebetween genes selected from the genes listed in Table 1-7, Table 1-16, Table 1-22, Table 1- 28, Table 1-34, and Table 1-40. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127,128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or any range therebetween genes selected from the genes listed in Table 1-7, and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of all the genes listed in Table 1-7 and Table 1-28. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of HERC6, OASL, ISG15, IFI27, IFITM1, IFI6, OAS2, HERC5, IFI35, IFIT3, OAS1, RSAD2, OAS3, EIF2AK2, IFI44L, ISG20, LGALS3BP, IL1RN, MX1, MX2, IFIT1, IFI44, SAMD9L, IFIT5, SP100, IFIT2, RPGR, SP110, RELB, MRPS15, NFKB1, TAP2, CYP27B1, PATJ, GBP1, ITGA6, BRCA1, NFKB2, TYROBP, FLNA, GBP4, SAMD9, CASP1, GMIP, AKAP10, SPI1, CXCL8, GLS, SH3BP5, KLF1, PTGS2, VEGFA, AK3, GBP2, UBE2C, SDC4, AMPD3, ASAP1, MARCKS, ACLY, DAB2, IFI30, CASP10, IL1B, ETAA1, EREG, BHMT, NR3C1, MGLL, CD38, CD37, TFF1, INSIGI, TNFRSF11A, TSC22D1, PIAS4, TRAF1, CKB, HHEX, TBX3, EBB, MAP3K4, MMP19, BHLHE41, INHBA, IFITM2, IL18, TNFAIP3, NFKBIA, HOMER2, APOL3, MSC, and EGR1. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of OAS2, OAS1, IFI27, MX1, EIF2AK2, IFI35, ISG15, IFI44L, IFI6, OAS3, OASL, HERC5, RSAD2, IFIT3, HERC6, SP100, IFI44, LGALS3BP, SAMD9L, IFIT1, MX2, IFITM1, IFIT2, IL1RN, SP110, IFIT5, GBP1, ISG20, TAP2, CASP1, GBP2, BRCA1, ITGA6, CASP10, SAMD9, GBP4, IFI30, CCL20, RPGR, PATJ, MTF1, UBE2C, KLF1, NFKB2, CD38, FLNA, SH3BP5, NR3C1, INHBA, PTGS2, AK3, HP, INSIGI, AMPD3, ASAP1, SLAMF1, VEGFA, SPI1, GLS, RELB, GMIP, ICAM1, AKAP10, TNFAIP3, TNFAIP2, SDC4, MRPS15, MGLL, IL16, ARID3A, STAT4, APOL3, G0S2, PLAUR, MMP19, CDKN3, EREG, EPB41, CR2, BHMT, NELL2, WT1, EGR1, ETAA1, MSC, FCER2, BIRC3, IFITM2, MAP3K4, TNFRSF11A, and NFKBIZ. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of LGALS3BP, HERC6, OAS1, IFI6, IFI35, ISG15, CR2, MX1, RELB, NFKB2, ISG20, HERC5, OAS3, IFI44L, RSAD2, OASL, SP100, IFIT3, OAS2, IFITM1, CD37, EIF2AK2, GBP4, IFI27, SP110, IFIT1, TAP2, SAMD9L, PTGS2, IFI44, MX2, CXCL8, TSC22D1, EREG, NFKB1, NAMPT, CD38, MSC, IFIT5, GBP1, CD83, IL1RN, GBP2, FCER2, NR3C1, SH3BP5, PATJ, MMP19, FLNA, MRPS15, and ITGA6. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of SP 110, CD37, IFI27, OAS2, MX2, IFI35, OAS1, CXCL2, ISG15, GBP4, LGALS3BP, ETAA1, NAMPT, OASL, RSAD2, PTGS2, SP100, IFI44L, IFIT3, BCL2A1, MX1, OAS3, SAMD9L, NR3C1, HERC6, CD38, MMP19, and RPGR. In certain embodiments, the patient data set comprises or is derived from gene expression measurement of at least 2 genes selected from the genes listed in each of one or more Tables selected from Table 1-7, Table 1-16, Table 1-22, Table 1-28, Table 1-34, and Table 1-40. In certain embodiments, the patient dataset comprises or is derived from gene expression measurements of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150 or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-7, Table 1-16, Table 1-22, Table 1-28, Table 1-34, and Table 1-40. In certain embodiments, Table 1-7 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7. In certain embodiments, Table 1-28 is selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7 and Table 1-28 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1- 7, and at least 2 genes selected from the genes listed in Table 1-28. In certain embodiments, Table 1-7, Table 1-34 and Table 1-40 are selected, e.g., the patient data set comprises at least 2 genes selected from the genes listed in Table 1-7, at least 2 genes selected from the genes listed in Table 1-34 and at least 2 genes selected from the genes listed in Table 1-40. In certain embodiments, Table 1-7, Table 1-22, and Table 1-40 are selected. In certain embodiments, Table 1-7 and Table 1-40 are selected. In certain embodiments, Table 1-7 and Table 1-16 are selected. In certain embodiments, Table 1-7 and Table 1- 34 are selected. In certain embodiments, Table 1-7 and Table 1-22 are selected. The gene expression measurement can be obtained from a biological sample obtained or derived from the patient.

[0134] In certain embodiments, analyzing the patient dataset includes providing the patient dataset as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus or rheumatoid arthritis; receiving, as an output of the machinelearning model the inference; and electronically outputting a report classifying whether the patient has lupus or rheumatoid arthritis based on the inference. The method can classify whether the patient has lupus or rheumatoid arthritis based on the inference. The machine -learning model can generate the inference, based on comparing the patient data set to a reference data set. The reference data set can comprise and / or be derived from gene expression measurements from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference subjects. In certain embodiments, the plurality of reference biological samples comprise i) a first plurality of the reference biological samples obtained or derived from reference subjects having lupus, and ii) a second plurality of the reference biological samples obtained or derived from reference subjects having rheumatoid arthritis. In certain embodiments, the reference data set comprises and / or is derived from gene expression measurements from the plurality of reference biological samples of at least 2 genes selected from the genes listed in Table 1-7, Table 1-16, Table 1-22, Table 1-28, Table 1-34, and Table 1-40. The selected genes of the patient data set (e.g., gene expression measurements of which the patient data set is comprised of or derived from), and the selected genes of the reference data set (e.g., gene expression measurements of which the reference dataset is comprised of or derived from) can at least partially overlap (e.g., one or more of the selected genes can be the same). In certain embodiments,selected genes of the patient data set, and selected genes of the reference data are same. In certain embodiments, selected genes of the patient data set, and selected genes of the reference data are same, and can be any selected set of genes e.g., of the patient data set, as described above or elsewhere herein. The Tables selected, and genes selected from a selected Table for the patient data set and the reference data set can be the same, and can be as described (e.g., for the patient data set) herein. In certain embodiments, the reference data set can be a reference data set as described in the Examples. The machine learning model can be trained with the reference data set. The machine learning model can be trained using a suitable method, and a suitable reference data set such that the trained machine learning model can generate the inference based on the patient data set, with a desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value. The desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, can be respectively an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value described above or elsewhere herein.

[0135] In certain embodiments, the patient is at elevated risk of having lupus or rheumatoid arthritis. In certain embodiments, the patient is suspected of having lupus or rheumatoid arthritis. In certain embodiments, the patient is asymptomatic for lupus or rheumatoid arthritis. In certain embodiments, the patient is at elevated risk of having an autoimmune disease. In certain embodiments, the patient is suspected of having an autoimmune disease. In certain embodiments, the patient is asymptomatic for an autoimmune disease. In certain embodiments the patient has an autoimmune disease.

[0136] In certain embodiments, the method further comprising selecting, recommending and / or administering a treatment to the patient based on the inference. In certain embodiments, the method further comprising administering a treatment to the patient based on the inference. In certain embodiments, the treatment comprises a pharmaceutical composition. In certain embodiments, the treatment is for lupus, e.g., when the inference is the patient data set is indicative of the patient having lupus. In certain embodiments, the treatment is for rheumatoid arthritis, e.g., when the inference is the patient data set is indicative of the patient having rheumatoid arthritis. The treatment can be for lupus or rheumatoid arthritis. The treatment for lupus can be for treating lupus, reduce a severity of lupus, and / or reduce a risk of having lupus. The treatment for rheumatoid arthritis can be for treating rheumatoid arthritis, reduce a severity of rheumatoid arthritis, and / or reduce a risk of having rheumatoid arthritis.

[0137] The patient data set (e.g., of methods described in detailed description section Ila-f) can be generated from the biological sample obtained or derived from the patient. For example, nucleic acid molecules of the patient in the biological sample can be assessed to obtain the patient data set. In certain embodiments, the gene expression measurements of the biological sample of the selected genes can be performed using any suitable method known to those of skill in the art including but not limited to DNA sequencing, RNA sequencing, microarray, RNA-Seq, qPCR, northern blotting, fluorescent in situ hybridization, serial analysis of gene expression, tiling arrays or any combination thereof, to obtain the data set. In certain embodiments, the gene expression measurements of the biological sample of the selected genes can be performed using RNA-Seq. RNA-seq can include single cell RNA-seq, and / or bulkRNA-seq. In certain embodiments, the gene expression measurements of the biological sample of the selected genes can be performed using microarray. In certain embodiments, the patient data set can be derived from the gene expression measurements of the biological sample of the selected genes, wherein the gene expression measurements is analyzed using a suitable data analysis tool including but not limited to a BIG-C™ big data analysis tool, an I-Scope™ big data analysis tool, a T-Scope™ big data analysis tool, a CellScan big data analysis tool, an MS (Molecular Signature) Scoring ™ analysis tool, gene set variation analysis (GSVA), Z-score, gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene co-expression network analysis (MEGENA), weighted gene co-expression network analysis (WGCNA), differential expression analysis, log2 expression analysis, or any combination thereof, to obtain the patient data set. In certain embodiments, the gene expression measurements of the biological sample of the selected genes can be analyzed using GSVA, to obtain the patient data set. In certain embodiments, the method comprises obtaining and / or deriving the biological sample from the patient. In certain embodiments, the method comprises analyzing the biological sample to obtain the gene expression measurements from the biological sample. In certain embodiments, the method comprises analyzing the gene expression measurements to obtain the patient data set. In certain embodiments, the method comprises obtaining and / or deriving the biological sample from the patient, and / or analyzing the biological sample to obtain the gene expression measurement from the biological sample. In certain embodiments, the method comprises obtaining and / or deriving the biological sample from the patient, analyzing the biological sample to obtain the gene expression measurement from the biological sample, and / or analyzing the gene expression measurements to obtain the patient data set.

[0138] The machine -learning model (e.g., of methods described in detailed description section Ila-f) can be trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, a Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof. The algorithm of the trained machine learning model can be a machine learning classifier, e.g., mentioned in this paragraph. The machine learning classifier (e.g., linear regression, LOG, Ridge regression, Lasso regression, EN regression, SVM, GBM, kNN, GLM, NB classifier, neural network, a RF, deep learning algorithm, LDA, DTREE, ADB, CART, and / or hierarchical clustering) can be trained to obtain the trained machine learning model. In some embodiments, the trained machine learning model, is trained using a supervised machine learning algorithm or an unsupervised machine learning algorithm, e.g., the classifier can be a supervised machine learning algorithm or an unsupervised machine learning algorithm. In certain embodiments, the trained machine learning model is trained using linear regression. In certain embodiments, the trained machine learning model is trained using LOG. In certain embodiments, the trained machine learning model is trained using Ridge regression. In certain embodiments, the trained machine learning model is trained using Lasso regression. In certain embodiments, the trained machine learning model is trained using EN.In certain embodiments, the trained machine learning model is trained using SVM. In certain embodiments, the machine learning model is trained using GBM. In certain embodiments, the trained machine learning model is trained using KNN. In certain embodiments, the trained machine learning model is trained using GLM. In certain embodiments, the trained machine learning model is trained using NB. In certain embodiments, the trained machine learning model is trained using RF. In certain embodiments, the trained machine learning model is trained using deep learning algorithm. In certain embodiments, the trained machine learning model is trained using LDA. In certain embodiments, the trained machine learning model is trained using DTREE. In certain embodiments, the trained machine learning model is trained using ADB. In certain embodiments, the trained machine learning model is trained using CART. In certain embodiments, the trained machine learning model is trained using hierarchical clustering.

[0139] In certain embodiments, the reference date (e.g., used fortraining the machine learning model of methods described in detailed description section Ila-f) set is normalized. In certain embodiments, the reference date set is culled of outliers, were cleaned of background noise and / or was normalized using Robust Multiarray Average (RMA), Guanine Cytosine Robust Multi-Array Analysis (GCRMA), or normexp background correction (NEQC) based on the microarray platform resulting in log2 transformed expression values. In certain embodiments, the reference date set is culled of outliers; were cleaned of background noise; were removed of data without annotation data; scaled; variance corrected and / or normalized. Normalizing can be performed using Robust Multiarray Average (RMA), Guanine Cytosine Robust Multi-Array Analysis (GCRMA), Linear Models for Microarray Data, variance stabilizing transformation (VST), or normexp background correction (NEQC) based on the microarray platform resulting in log2 transformed expression values. In certain embodiments, the trained machine learning model can be trained using a method, and / or a reference data set as described in the Examples. In certain embodiments, a first portion of the reference data set can be used as training data set, and a second portion of the reference data set can be used as validation data set, for training the machine learning model. In certain embodiments, 0 to 25 fold, such as 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 fold cross-validation is used. In certain embodiments, 6 fold cross-validation is used. In certain embodiments, 10 fold cross-validation is used. In certain embodiments, oversampling or undersampling correction is made during training of the machine learning model. Synthetic Minority Oversampling Technique (SMOTE) can be applied on the training data to handle class imbalances.

[0140] In some embodiments, the reference biological samples (e.g., of methods described in detailed description section Ila-f) comprise blood samples, isolated peripheral blood mononuclear cells (PBMCs), tissue biopsy samples, nasal fluid samples, saliva samples, or any derivative thereof. In some embodiments, the reference biological samples comprise blood samples or any derivative thereof. In some embodiments, the reference biological samples comprise isolated peripheral blood mononuclear cells (PBMCs) or any derivative thereof. In some embodiments, the reference biological samples comprise tissue biopsy samples or any derivative thereof. Tissue can be skin tissue, or kidney tissue. The reference subjects can be human.

[0141] The inference (e.g., of machine learning model of the methods described in detailed description section Ila-f) can have a confidence value between 0 and 1. In certain embodiments, the confidence value of the inference is between 0 and 1, such as, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or 1, or any value or ranges there between.

[0142] The machine -learning model (e.g., of methods described in detailed description section Ila-f) can have a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) of at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more than about 0.99, for classifying the disease state of the reference subjects. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.85 to about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.85 to about 0.9, about 0.85 to about 0.92, about 0.85 to about 0.94, about 0.85 to about 0.95, about 0.85 to about 0.96, about 0.85 to about 0.98, about 0.85 to about 0.99, about 0.85 to about 0.993, about 0.85 to about 0.995, about 0.85 to about 0.998, about 0.85 to about 1, about 0.9 to about 0.92, about 0.9 to about 0.94, about 0.9 to about 0.95, about 0.9 to about 0.96, about 0.9 to about 0.98, about 0.9 to about 0.99, about 0.9 to about 0.993, about 0.9 to about 0.995, about 0.9 to about 0.998, about 0.9 to about 1, about 0.92 to about 0.94, about 0.92 to about 0.95, about 0.92 to about 0.96, about 0.92 to about 0.98, about 0.92 to about 0.99, about 0.92 to about 0.993, about 0.92 to about 0.995, about 0.92 to about 0.998, about 0.92 to about 1, about 0.94 to about 0.95, about 0.94 to about 0.96, about 0.94 to about 0.98, about 0.94 to about 0.99, about 0.94 to about 0.993, about 0.94 to about 0.995, about 0.94 to about 0.998, about 0.94 to about 1, about 0.95 to about 0.96, about 0.95 to about 0.98, about 0.95 to about 0.99, about 0.95 to about 0.993, about 0.95 to about 0.995, about 0.95 to about 0.998, about 0.95 to about 1, about 0.96 to about 0.98, about 0.96 to about 0.99, about 0.96 to about 0.993, about 0.96 to about 0.995, about 0.96 to about 0.998, about 0.96 to about 1, about 0.98 to about 0.99, about 0.98 to about 0.993, about 0.98 to about 0.995, about 0.98 to about 0.998, about 0.98 to about 1, about 0.99 to about 0.993, about 0.99 to about 0.995, about 0.99 to about 0.998, about 0.99 to about 1, about 0.993 to about 0.995, about 0.993 to about 0.998, about 0.993 to about 1, about 0.995 to about 0.998, about 0.995 to about 1, or about 0.998 to about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.85, about 0.9, about 0.92, about 0.94, about 0.95, about 0.96, about 0.98, about 0.99, about 0.993, about 0.995, about 0.998, or about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of at least about 0.85, about 0.9, about 0.92, about 0.94, about 0.95, about 0.96, about 0.98, about 0.99, about 0.993, about 0.995, or about 0.998. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.7 to about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.7 to about 0.75, about 0.7 to about 0.8, about 0.7 to about 0.85, about 0.7 to about 0.9, about 0.7 to about 0.95, about 0.7 to about 0.975, about 0.7 to about 1, about 0.75 to about 0.8, about 0.75 to about 0.85, about 0.75 to about 0.9, about 0.75 to about 0.95, about 0.75 to about 0.975, about 0.75 to about 1, about 0.8 to about 0.85, about 0.8 to about 0.9, about 0.8 to about 0.95, about 0.8 to about 0.975, about 0.8 to about 1, about 0.85 to about 0.9, about 0.85 to about 0.95, about 0.85 to about 0.975, about 0.85 to about 1, about 0.9 toabout 0.95, about 0.9 to about 0.975, about 0.9 to about 1, about 0.95 to about 0.975, about 0.95 to about 1, or about 0.975 to about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.95, about 0.975, or about 1. In some embodiments, the machine learning model has a ROC curve with an AUC of at least about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.95, or about 0.975. The ROC curve can be for classifying the disease state of the reference subjects. Classifying the disease state of a reference subject can include classifying i) whether the reference subject has lupus, ii) whether the reference subject has inactive lupus, iii) whether the reference subject has active lupus or inactive lupus, iv) whether the reference subject has lupus nephritis, v) whether the reference subject has lupus nephritis or non-renal lupus, or vi) whether the reference subject has lupus or rheumatoid arthritis.

[0143] The method (e.g., described in detailed description section Ila-f) can classify the disease state of the patient with an accuracy of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify the disease state of the patient with a sensitivity of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify the disease state of the patient with a specificity of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify the disease state of the patient with a positive predictive value of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. The method can classify the disease state of the patient with a negative predictive value of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%. In some embodiments, the method classify the disease state of the patient with an accuracy of about 85 % to about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of about 85 % to about 90 %, about 85 % to about 92 %, about 85 % to about 94 %, about 85 % to about 95 %, about 85 % to about 96 %, about 85 % to about 98 %, about 85 % to about 99 %, about 85 % to about 99.3 %, about 85 % to about 99.5 %, about 85 % to about 99.8 %, about 85 % to about 100 %, about 90 % to about 92 %, about 90 % to about 94 %, about 90 % to about 95 %, about 90 % to about 96 %, about 90 % to about 98 %, about 90 % to about 99 %, about 90 % to about 99.3 %, about 90 % to about 99.5 %, about 90 % to about 99.8 %, about 90 % to about 100 %, about 92 % to about 94 %, about 92 % to about 95 %, about 92 % to about 96 %, about 92 % to about 98 %, about 92 % to about 99 %, about 92 % to about 99.3 %,about 92 % to about 99.5 %, about 92 % to about 99.8 %, about 92 % to about 100 %, about 94 % to about 95 %, about 94 % to about 96 %, about 94 % to about 98 %, about 94 % to about 99 %, about 94 % to about 99.3 %, about 94 % to about 99.5 %, about 94 % to about 99.8 %, about 94 % to about 100 %, about 95 % to about 96 %, about 95 % to about 98 %, about 95 % to about 99 %, about 95 % to about 99.3 %, about 95 % to about 99.5 %, about 95 % to about 99.8 %, about 95 % to about 100 %, about 96 % to about 98 %, about 96 % to about 99 %, about 96 % to about 99.3 %, about 96 % to about 99.5 %, about 96 % to about 99.8 %, about 96 % to about 100 %, about 98 % to about 99 %, about 98 % to about 99.3 %, about 98 % to about 99.5 %, about 98 % to about 99.8 %, about 98 % to about 100 %, about 99 % to about 99.3 %, about 99 % to about 99.5 %, about 99 % to about 99.8 %, about 99 % to about 100 %, about 99.3 % to about 99.5 %, about 99.3 % to about 99.8 %, about 99.3 % to about 100 %, about 99.5 % to about 99.8 %, about 99.5 % to about 100 %, or about 99.8 % to about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, about 99.8 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the method classify the lupus disease state of the patient with an accuracy of about 70 % to about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of about 70 % to about 75 %, about 70 % to about 80 %, about 70 % to about 85 %, about 70 % to about 90 %, about 70 % to about 95 %, about 70 % to about 97.5 %, about 70 % to about 100 %, about 75 % to about 80 %, about 75 % to about 85 %, about 75 % to about 90 %, about 75 % to about 95 %, about 75 % to about 97.5 %, about 75 % to about 100 %, about 80 % to about 85 %, about 80 % to about 90 %, about 80 % to about 95 %, about 80 % to about 97.5 %, about 80 % to about 100 %, about 85 % to about 90 %, about 85 % to about 95 %, about 85 % to about 97.5 %, about 85 % to about 100 %, about 90 % to about 95 %, about 90 % to about 97.5 %, about 90 % to about 100 %, about 95 % to about 97.5 %, about 95 % to about 100 %, or about 97.5 % to about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, about 97.5 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with an accuracy of at least about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, or about 97.5 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 85 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 85 % to about 90 %, about 85 % to about 92 %, about 85 % to about 94 %, about 85 % to about 95 %, about 85 % to about 96 %, about 85 % to about 98 %, about 85 % to about 99 %, about 85 % to about 99.3 %, about 85 % to about 99.5 %, about 85 % to about 99.8 %, about 85 % to about 100 %, about 90 % to about 92 %, about 90 % to about 94 %, about 90 % to about 95 %, about 90 % to about 96 %, about 90 % to about 98 %, about 90 % to about 99 %, about 90 % to about 99.3 %, about 90 % to about 99.5 %, about 90 % to about 99.8 %, about 90 % to about 100 %, about 92 % to about 94 %, about 92 % to about 95 %, about 92 % to about 96 %, about 92 % to about 98 %, about 92 %to about 99 %, about 92 % to about 99.3 %, about 92 % to about 99.5 %, about 92 % to about 99.8 %, about 92 % to about 100 %, about 94 % to about 95 %, about 94 % to about 96 %, about 94 % to about 98 %, about 94 % to about 99 %, about 94 % to about 99.3 %, about 94 % to about 99.5 %, about 94 % to about 99.8 %, about 94 % to about 100 %, about 95 % to about 96 %, about 95 % to about 98 %, about 95 % to about 99 %, about 95 % to about 99.3 %, about 95 % to about 99.5 %, about 95 % to about 99.8 %, about 95 % to about 100 %, about 96 % to about 98 %, about 96 % to about 99 %, about 96 % to about 99.3 %, about 96 % to about 99.5 %, about 96 % to about 99.8 %, about 96 % to about 100 %, about 98 % to about 99 %, about 98 % to about 99.3 %, about 98 % to about 99.5 %, about 98 % to about 99.8 %, about 98 % to about 100 %, about 99 % to about 99.3 %, about 99 % to about 99.5 %, about 99 % to about 99.8 %, about 99 % to about 100 %, about 99.3 % to about 99.5 %, about 99.3 % to about 99.8 %, about 99.3 % to about 100 %, about 99.5 % to about 99.8 %, about 99.5 % to about 100 %, or about 99.8 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, about 99.8 %, or about 100 %. In some embodiments, the method classify the lupus disease state of the patient with a sensitivity of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 70 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 70 % to about 75 %, about 70 % to about 80 %, about 70 % to about 85 %, about 70 % to about 90 %, about 70 % to about 95 %, about 70 % to about 97.5 %, about 70 % to about 100 %, about 75 % to about 80 %, about 75 % to about 85 %, about 75 % to about 90 %, about 75 % to about 95 %, about 75 % to about 97.5 %, about 75 % to about 100 %, about 80 % to about 85 %, about 80 % to about 90 %, about 80 % to about 95 %, about 80 % to about 97.5 %, about 80 % to about 100 %, about 85 % to about 90 %, about 85 % to about 95 %, about 85 % to about 97.5 %, about 85 % to about 100 %, about 90 % to about 95 %, about 90 % to about 97.5 %, about 90 % to about 100 %, about 95 % to about 97.5 %, about 95 % to about 100 %, or about 97.5 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, about 97.5 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a sensitivity of at least about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, or about 97.5 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 85 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 85 % to about 90 %, about 85 % to about 92 %, about 85 % to about 94 %, about 85 % to about 95 %, about 85 % to about 96 %, about 85 % to about 98 %, about 85 % to about 99 %, about 85 % to about 99.3 %, about 85 % to about 99.5 %, about 85 % to about 99.8 %, about 85 % to about 100 %, about 90 % to about 92 %, about 90 % to about 94 %, about 90 % to about 95 %, about 90 % to about 96 %, about 90 % to about 98 %, about 90 % to about 99 %, about 90 % to about 99.3 %, about 90 % to about 99.5 %, about 90 % to about 99.8 %, about 90 % to about 100 %, about 92 % to about 94 %, about 92 % to about 95 %, about 92% to about 96 %, about 92 % to about 98 %, about 92 % to about 99 %, about 92 % to about 99.3 %, about 92 % to about 99.5 %, about 92 % to about 99.8 %, about 92 % to about 100 %, about 94 % to about 95 %, about 94 % to about 96 %, about 94 % to about 98 %, about 94 % to about 99 %, about 94 % to about 99.3 %, about 94 % to about 99.5 %, about 94 % to about 99.8 %, about 94 % to about 100 %, about 95 % to about 96 %, about 95 % to about 98 %, about 95 % to about 99 %, about 95 % to about 99.3 %, about 95 % to about 99.5 %, about 95 % to about 99.8 %, about 95 % to about 100 %, about 96 % to about 98 %, about 96 % to about 99 %, about 96 % to about 99.3 %, about 96 % to about 99.5 %, about 96 % to about 99.8 %, about 96 % to about 100 %, about 98 % to about 99 %, about 98 % to about 99.3 %, about 98 % to about 99.5 %, about 98 % to about 99.8 %, about 98 % to about 100 %, about 99 % to about 99.3 %, about 99 % to about 99.5 %, about 99 % to about 99.8 %, about 99 % to about 100 %, about 99.3 % to about 99.5 %, about 99.3 % to about 99.8 %, about 99.3 % to about 100 %, about 99.5 % to about 99.8 %, about 99.5 % to about 100 %, or about 99.8 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, about 99.8 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 70 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 70 % to about 75 %, about 70 % to about 80 %, about 70 % to about 85 %, about 70 % to about 90 %, about 70 % to about 95 %, about 70 % to about 97.5 %, about 70 % to about 100 %, about 75 % to about 80 %, about 75 % to about 85 %, about 75 % to about 90 %, about 75 % to about 95 %, about 75 % to about 97.5 %, about 75 % to about 100 %, about 80 % to about 85 %, about 80 % to about 90 %, about 80 % to about 95 %, about 80 % to about 97.5 %, about 80 % to about 100 %, about 85 % to about 90 %, about 85 % to about 95 %, about 85 % to about 97.5 %, about 85 % to about 100 %, about 90 % to about 95 %, about 90 % to about 97.5 %, about 90 % to about 100 %, about 95 % to about 97.5 %, about 95 % to about 100 %, or about 97.5 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, about 97.5 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a specificity of at least about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, or about 97.5 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 85 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 85 % to about 90 %, about 85 % to about 92 %, about 85 % to about 94 %, about 85 % to about 95 %, about 85 % to about 96 %, about 85 % to about 98 %, about 85 % to about 99 %, about 85 % to about 99.3 %, about 85 % to about 99.5 %, about 85 % to about 99.8 %, about 85 % to about 100 %, about 90 % to about 92 %, about 90 % to about 94 %, about 90 % to about 95 %, about 90 % to about 96 %, about 90 % to about 98 %, about 90 % to about 99 %, about 90 % to about 99.3 %, about 90 % to about 99.5 %, about 90 % to about 99.8 %, about 90 % to about 100 %, about 92 % toabout 94 %, about 92 % to about 95 %, about 92 % to about 96 %, about 92 % to about 98 %, about 92 % to about 99 %, about 92 % to about 99.3 %, about 92 % to about 99.5 %, about 92 % to about 99.8 %, about 92 % to about 100 %, about 94 % to about 95 %, about 94 % to about 96 %, about 94 % to about 98 %, about 94 % to about 99 %, about 94 % to about 99.3 %, about 94 % to about 99.5 %, about 94 % to about 99.8 %, about 94 % to about 100 %, about 95 % to about 96 %, about 95 % to about 98 %, about 95 % to about 99 %, about 95 % to about 99.3 %, about 95 % to about 99.5 %, about 95 % to about 99.8 %, about 95 % to about 100 %, about 96 % to about 98 %, about 96 % to about 99 %, about 96 % to about 99.3 %, about 96 % to about 99.5 %, about 96 % to about 99.8 %, about 96 % to about 100 %, about 98 % to about 99 %, about 98 % to about 99.3 %, about 98 % to about 99.5 %, about 98 % to about 99.8 %, about 98 % to about 100 %, about 99 % to about 99.3 %, about 99 % to about 99.5 %, about 99 % to about 99.8 %, about 99 % to about 100 %, about 99.3 % to about 99.5 %, about 99.3 % to about 99.8 %, about 99.3 % to about 100 %, about 99.5 % to about 99.8 %, about 99.5 % to about 100 %, or about 99.8 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, about 99.8 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 70 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 70 % to about 75 %, about 70 % to about 80 %, about 70 % to about 85 %, about 70 % to about 90 %, about 70 % to about 95 %, about 70 % to about 97.5 %, about 70 % to about 100 %, about 75 % to about 80 %, about 75 % to about 85 %, about 75 % to about 90 %, about 75 % to about 95 %, about 75 % to about 97.5 %, about 75 % to about 100 %, about 80 % to about 85 %, about 80 % to about 90 %, about 80 % to about 95 %, about 80 % to about 97.5 %, about 80 % to about 100 %, about 85 % to about 90 %, about 85 % to about 95 %, about 85 % to about 97.5 %, about 85 % to about 100 %, about 90 % to about 95 %, about 90 % to about 97.5 %, about 90 % to about 100 %, about 95 % to about 97.5 %, about 95 % to about 100 %, or about 97.5 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, about 97.5 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a positive predictive value of at least about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, or about 97.5 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 85 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 85 % to about 90 %, about 85 % to about 92 %, about 85 % to about 94 %, about 85 % to about 95 %, about 85 % to about 96 %, about 85 % to about 98 %, about 85 % to about 99 %, about 85 % to about 99.3 %, about 85 % to about 99.5 %, about 85 % to about 99.8 %, about 85 % to about 100 %, about 90 % to about 92 %, about 90 % to about 94 %, about 90 % to about 95 %, about 90 % to about 96 %, about 90 % to about 98 %, about 90 % toabout 99 %, about 90 % to about 99.3 %, about 90 % to about 99.5 %, about 90 % to about 99.8 %, about 90 % to about 100 %, about 92 % to about 94 %, about 92 % to about 95 %, about 92 % to about 96 %, about 92 % to about 98 %, about 92 % to about 99 %, about 92 % to about 99.3 %, about 92 % to about 99.5 %, about 92 % to about 99.8 %, about 92 % to about 100 %, about 94 % to about 95 %, about 94 % to about 96 %, about 94 % to about 98 %, about 94 % to about 99 %, about 94 % to about 99.3 %, about94 % to about 99.5 %, about 94 % to about 99.8 %, about 94 % to about 100 %, about 95 % to about 96 %, about 95 % to about 98 %, about 95 % to about 99 %, about 95 % to about 99.3 %, about 95 % to about 99.5 %, about 95 % to about 99.8 %, about 95 % to about 100 %, about 96 % to about 98 %, about 96 % to about 99 %, about 96 % to about 99.3 %, about 96 % to about 99.5 %, about 96 % to about 99.8 %, about 96 % to about 100 %, about 98 % to about 99 %, about 98 % to about 99.3 %, about 98 % to about 99.5 %, about 98 % to about 99.8 %, about 98 % to about 100 %, about 99 % to about 99.3 %, about 99 % to about 99.5 %, about 99 % to about 99.8 %, about 99 % to about 100 %, about 99.3 % to about 99.5 %, about 99.3 % to about 99.8 %, about 99.3 % to about 100 %, about 99.5 % to about 99.8 %, about 99.5 % to about 100 %, or about 99.8 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, about 99.8 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 70 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 70 % to about 75 %, about 70 % to about 80 %, about 70 % to about 85 %, about 70 % to about 90 %, about 70 % to about 95 %, about 70 % to about 97.5 %, about 70 % to about 100 %, about 75 % to about 80 %, about 75 % to about 85 %, about 75 % to about 90 %, about 75 % to about 95 %, about 75 % to about 97.5 %, about 75 % to about 100 %, about 80 % to about 85 %, about 80 % to about 90 %, about 80 % to about 95 %, about 80 % to about 97.5 %, about 80 % to about 100 %, about 85 % to about 90 %, about 85 % to about 95 %, about 85 % to about 97.5 %, about 85 % to about 100 %, about 90 % to about95 %, about 90 % to about 97.5 %, about 90 % to about 100 %, about 95 % to about 97.5 %, about 95 % to about 100 %, or about 97.5 % to about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, about 97.5 %, or about 100 %. In some embodiments, the method classify the disease state of the patient with a negative predictive value of at least about 70 %, about 75 %, about 80 %, about 85 %, about 90 %, about 95 %, or about 97.5 %. Classifying the disease state of the patient can include classifying i) whether the patient has lupus, ii) whether the patient has inactive lupus, iii) whether the patient has active lupus or inactive lupus, iv) whether the patient has lupus nephritis, v) whether the patient has lupus nephritis or non-renal lupus, or vi) whether the patient has lupus or rheumatoid arthritis.

[0144] The machine -learning model can have the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and ROC-AUC value, described above, and the accuracy, sensitivity,specificity, positive predictive value, and negative predictive value, of the method can be based on the classification parameters of the machine-learning model, as described above or elsewhere herein and / or as understood by one of skill in the art. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, of the method can be calculated based on the ROC-AUC curve of the machine model for classification of disease state of reference subjects / patients. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and / or ROC-AUC value, can have a value as shown in the Examples.

[0145] The biological sample (e.g., obtained and / or derived from the patient) can comprise a blood sample, isolated peripheral blood mononuclear cells (PBMCs), tissue biopsy sample, nasal fluid, saliva, urine, stool, or any derivative thereof. In certain embodiments, the biological sample comprises a blood sample or any derivative thereof. In certain embodiments, the biological sample comprises isolated PBMCs, or any derivative thereof. In certain embodiments, the biological sample comprises tissue biopsy sample, or any derivative thereof. The tissue can be skin tissue, or kidney tissue. In certain embodiments, the biological sample comprises nasal fluid sample, or any derivative thereof. In certain embodiments, the biological sample comprises saliva sample, or any derivative thereof. In certain embodiments, the biological sample comprises urine sample, or any derivative thereof. In certain embodiments, the biological sample comprises stool sample, or any derivative thereof. The patient can be a human patient.

[0146] In certain embodiments, the method further comprises monitoring the disease state of the patient, wherein the monitoring comprises assessing (e.g., classifying) the disease state of the patient at a plurality of different time points. A difference in the assessment of the disease state of the patient among the plurality of time points can be indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of the disease state of the patient, (ii) a prognosis of the disease state of the patient, and (iii) an efficacy or non-efficacy of a course of treatment for treating the disease state of the patient. In certain embodiments, the patient has been administered a treatment, and the method can assess an efficacy or non-efficacy of the treatment, for treating the disease state of the patient.

[0147] The method can determine whether a patient is a candidate for treatment with the drug based on the disease state classification of the patient.

[0148] Lupus can be any type of lupus including but not limited to systemic lupus erythematosus (SLE), cutaneous lupus erythematosus, drug-induced lupus, and neonatal lupus. In certain embodiments lupus can be SLE.

[0149] One aspect of the present disclosure is directed to the use of a patient data set described above or elsewhere herein.III. Treatments

[0150] In certain embodiments, methods described herein, such methods described in summary section, detailed description sections I and Ila-f, can include selecting, recommending and / or administering a treatment to the patient based on the inference based on patient data set. In certain embodiments, methodsdescribed herein, such methods described in summary section, detailed description section I and Ila-f, can include administering a treatment to the patient based on the inference based on patient data set. The treatment can be any treatment undergoing testing for, or approved for, treatment of an inferred disease state. In certain embodiments, the treatment is for lupus, such as when the inference is the patient data set is indicative of the patient having lupus. In certain embodiments, the treatment is for active lupus, such as when the inference is the patient data set is indicative of the patient having active lupus. In certain embodiments, the treatment is for inactive lupus, such as when the inference is the patient data set is indicative of the patient having inactive lupus. In certain embodiments, the treatment is for lupus nephritis, such as when the inference is the patient data set is indicative of the patient having lupus nephritis. In certain embodiments, the treatment is for non-renal lupus, such as when the inference is the patient data set is indicative of the patient having non-renal lupus. In certain embodiments, the treatment is for rheumatoid arthritis such as when the inference is the patient data set is indicative of the patient having rheumatoid arthritis. Various treatments for lupus, active lupus, inactive lupus, lupus nephritis, and non-renal lupus are identified in Fanouriakis A, et al, 2019 update of the EULAR recommendations for the management of systemic lupus erythematosus, Annals of the Rheumatic Diseases, 2019; 78: 736-745, and Guidelines for referral and management of systemic lupus erythematosus in adults. American College of Rheumatology Ad Hoc Committee on Systemic Lupus Erythematosus Guidelines. Arthritis Rheum. 1999 Sep; 42(9): 1785-96, all of which are incorporated in full herein by reference. Various treatments for rheumatoid arthritis are identified in https: / / www.mayoclinic.org / diseases-conditions / rheumatoid- arthritis / diagnosis-treatment / drc-20353653, which is incorporated in full herein by reference. In certain embodiments, the treatment for lupus can include a neutrophil function inhibitor, a TNF inhibitor, an IL1 inhibitor, a Plasma cell inhibitor, a NK cell inhibitor, a B Cell Inhibitor, an IFN inhibitor, or any combination thereof. Non-limiting examples of an IFN inhibitor include Anifrolumab. Non-limiting examples of a Plasma cell inhibitor include Mycophenolate, Bortezomib, Carfilzomib, Ixazomib, Daratumumab, Isatuximab and Elotuzumab. Mycophenolate can be Mycophenolate Mofetil. Non-limiting examples of an IL1 inhibitor include Anakinra, and Canakinumab. Non-limiting examples of a TNF inhibitor include Adalimumab, Certolizumab pegol, Etanercept, Golimumab, and Infliximab. Nonlimiting examples of a Neutrophil function inhibitor include Dasatinib, Apremilast, and Roflumilast. Nonlimiting examples of a NK cell inhibitor include Azathioprine. Non-limiting examples of a B cell inhibitor include Belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, and Inebilizumab. In certain embodiments, the treatment for lupus comprises Anifrolumab, Mycophenolate, Bortezomib, Carfilzomib, Ixazomib, Daratumumab, Isatuximab, Elotuzumab, Anakinra, Canakinumab Adalimumab, Certolizumab pegol, Etanercept, Golimumab, Infliximab, Dasatinib, Apremilast, Roflumilast, Azathioprine,Belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, or any combination thereof. In certain embodiments, the treatment for active lupus can include a neutrophil function inhibitor, a TNF inhibitor, an IL1 inhibitor, a Plasma cell inhibitor, a NK cell inhibitor, a B Cell Inhibitor, an IFN inhibitor, or any combination thereof. In certain embodiments, the treatment for active lupus comprises Anifrolumab, Mycophenolate, Bortezomib, Carfilzomib, Ixazomib, Daratumumab, Isatuximab,Elotuzumab, Anakinra, Canakinumab Adalimumab, Certolizumab pegol, Etanercept, Golimumab, Infliximab, Dasatinib, Apremilast, Roflumilast, Azathioprine, Belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises a neutrophil function inhibitor, a TNF inhibitor, an IL1 inhibitor, a Plasma cell inhibitor, a NK cell inhibitor, a B Cell Inhibitor, an IFN inhibitor, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises Anifrolumab, Mycophenolate, Bortezomib, Carfilzomib, Ixazomib, Daratumumab, Isatuximab, Elotuzumab, Anakinra, Canakinumab Adalimumab, Certolizumab pegol, Etanercept, Golimumab, Infliximab, Dasatinib, Apremilast, Roflumilast, Azathioprine, Belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises a neutrophil function inhibitor, a TNF inhibitor, an IL1 inhibitor, a Plasma cell inhibitor, aNK cell inhibitor, a B Cell Inhibitor, an IFN inhibitor, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises Anifrolumab, Mycophenolate, Bortezomib, Carfdzomib, Ixazomib, Daratumumab, Isatuximab, Elotuzumab, Anakinra, Canakinumab Adalimumab, Certolizumab pegol, Etanercept, Golimumab, Infliximab, Dasatinib, Apremilast, Roflumilast, Azathioprine, Belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, or any combination thereof.In certain embodiments, the treatment for lupus nephritis comprises IFN inhibitor. In certain embodiments, the treatment for lupus nephritis comprises an IFN inhibitor, a Th 17 cell inhibitor, a B cell inhibitor, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises an IFN inhibitor, a TNF inhibitor, a Th 17 cell inhibitor, a B cell inhibitor, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises anifrolumab, secukinumab, ibrutinib, or the like. In certain embodiments, the treatment for lupus nephritis comprises anifrolumab, secukinumab, ibrutinib, or the like. In certain embodiments, the treatment for lupus nephritis comprises anifrolumab, secukinumab, ibrutinib, belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises anifrolumab, secukinumab, ibrutinib, belimumab, Rituximab, Obinutuzumab, Ocrelizumab, Ofatumumab, Inebilizumab, Adalimumab, Certolizumab pegol, Etanercept, Golimumab, and Infliximab or any combination thereof. In certain embodiments, the treatment for lupus nephritis comprises kidney transplantation. In certain embodiments, the treatment for non-renal lupus comprises antiphospholipid antibodies, antiplatelates, lipid, glucose, azathioprine, belimumab, calcineurin inhibitors, cyclophosphamide, glucocorticoids, hydroxychloroquine; mycophenolate mofetil, methotrexate, prednisone, rituximab, or any combination thereof. In certain embodiments, the treatment for rheumatoid arthritis can include nonsteroidal anti-inflammatory drugs; corticosteroids such as prednisone; methotrexate; leflunomide; hydroxychloroquine; sulfasalazine; abatacept; adalimumab; anakinra; certolizumab; etanercept; golimumab; infliximab; rituximab; sarilumab; tocilizumab; Baricitinib; tofacitinib; upadacitinib; surgery such as synovectomy, tendon repair joint fusion, and / or total joint replacement; or any combination thereof.Non-transitory computer readable medium

[0151] Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.System comprising one or more computer processors and computer memory coupled thereto

[0152] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.Digital Processing Device

[0153] In some embodiments, the platforms, systems, media, and methods described herein include a digital processing device, or use of the same. In further embodiments, the digital processing device includes one or more hardware central processing units (CPUs) or general purpose graphics processing units (GPGPUs) that carry out the device’s functions. In still further embodiments, the digital processing device further comprises an operating system configured to perform executable instructions. In some embodiments, the digital processing device is optionally connected a computer network. In further embodiments, the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web. In still further embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.

[0154] In accordance with the description herein, suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art.

[0155] In some embodiments, the digital processing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple®Mac OS X®, UNIX®, and UNIX -like operating systems such as GNU / Uinux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Uinux®, and Palm® WebOS®. Those of skill in the art will also recognize that suitable media streaming device operating systems include, by way of non-limiting examples, Apple TV®, Roku®, Boxee®, Google TV®, Google Chromecast®, Amazon Fire®, and Samsung® HomeSync®. Those of skill in the art will also recognize that suitable video game console operating systems include, by way of non-limiting examples, Sony® PS3®, Sony® PS4®, Microsoft® Xbox 360®, Microsoft Xbox One, Nintendo® Wii®, Nintendo® Wii U®, and Ouya®.

[0156] In some embodiments, the device includes a storage and / or memory device. The storage and / or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis. In some embodiments, the device is volatile memory and requires power to maintain stored information. In some embodiments, the device is non-volatile memory and retains stored information when the digital processing device is not powered. In further embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non-volatile memory comprises dynamic random-access memory (DRAM). In some embodiments, the non-volatile memory comprises ferroelectric random access memory (FRAM). In some embodiments, the non-volatile memory comprises phasechange random access memory (PRAM). In other embodiments, the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing -based storage. In further embodiments, the storage and / or memory device is a combination of devices such as those disclosed herein.

[0157] In some embodiments, the digital processing device includes a display to send visual information to a user. In some embodiments, the display is a liquid crystal display (UCD). In further embodiments, the display is a thin film transistor liquid crystal display (TFT-UCD). In some embodiments, the display is an organic light emitting diode (OUED) display. In various further embodiments, on OEED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display is a plasma display. In other embodiments, the display is a video projector. In yet other embodiments, the display is a head-mounted display in communication with the digital processing device, such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0158] In some embodiments, the digital processing device includes an input device to receive information from a user. In some embodiments, the input device is a keyboard. In some embodiments, the input device is a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus. In some embodiments, the input device is a touch screen or amulti-touch screen. In other embodiments, the input device is a microphone to capture voice or other sound input. In other embodiments, the input device is a video camera or other sensor to capture motion or visual input. In further embodiments, the input device is a Kinect, Leap Motion, or the like. In still further embodiments, the input device is a combination of devices such as those disclosed herein.Non-transitory computer readable storage medium

[0159] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked digital processing device. In further embodiments, a computer readable storage medium is a tangible component of a digital processing device. In still further embodiments, a computer readable storage medium is optionally removable from a digital processing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.Computer Program

[0160] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable in the digital processing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

[0161] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.Web application

[0162] In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, aweb application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of nonlimiting examples, relational, non-relational, object oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, clientside scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Elash® Actionscript, Javascript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tel, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.Standalone Application

[0163] In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.Web Browser Plug-in

[0164] In some embodiments, the computer program includes a web browser plug-in (e.g., extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and toreduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®.

[0165] In view of the disclosure provided herein, those of skill in the art will recognize that several plugin frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

[0166] Web browsers (also called Internet browsers) are software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non-limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called mircrobrowsers, mini-browsers, and wireless browsers) are designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.Software Modules

[0167] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on cloud computing platforms. In some embodiments, software modules are hosted on one or moremachines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.Databases

[0168] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for identifying one or more records having a specific phenotype. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entityrelationship model databases, associative databases, and XML databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, and Sybase. In some embodiments, a database is internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In other embodiments, a database is based on one or more local computer storage devices.Biological Data Analysis

[0169] Certain embodiments, of the present disclosure provides systems and methods to perform data analysis using drug or target scoring algorithms and / or big data analysis tools. In various aspects, such drug or target scoring algorithms and / or big data analysis tools may be used to perform analysis of data sets including, for example, mRNA gene expression or transcriptome data, DNA genomic data, proteomic data, metabolomic data, other types of “-omic” data, or a combination thereof.

[0170] In an aspect, the present disclosure provides a computer-implemented method for assessing a condition of a subject, comprising: (a) receiving a dataset of a biological sample of the subject; (b) selecting one or more data analysis tools, wherein the one or more data analysis tools comprise an analysis tool selected from the group consisting of : a BIG-C™ big data analysis tool, an I-Scope™ big data analysis tool, a T-Scope™ big data analysis tool, a CellScan big data analysis tool, an MS (Molecular Signature) Scoring ™ analysis tool, a Gene Set Variation Analysis (GSVA) tool (e.g., P-Scope), a CoLTs® (Combined Lupus Treatment Scoring) analysis tool, and a Target Scoring analysis tool; (c) processing the dataset using the one or more data analysis tools to generate a data signature of the biological sample of the subject; and (d) based at least in part on the data signature generated in (c), assessing the condition of the subject.

[0171] In some embodiments, the dataset comprises mRNA gene expression or transcriptome data, DNA genomic data, proteomic data, metabolomic data, or a combination thereof. In some embodiments, the biological sample is selected from the group consisting of: a whole blood (WB) sample, a PBMC sample, a tissue sample, and a cell sample. In some embodiments, assessing the condition of the subject comprises identifying a disease or disorder of the subject.

[0172] In some embodiments, the method further comprises identifying a disease or disorder of the subject at a sensitivity or specificity of at least about 70%. In some embodiments, the method furthercomprises determining a likelihood of the identification of the disease or disorder of the subject. In some embodiments, the method further comprises providing a therapeutic intervention for the disease or disorder of the subject. In some embodiments, the method further comprises monitoring the disease or disorder of the subject, wherein the monitoring comprises assessing the disease or disorder of the subject at a plurality of time points, wherein the assessing is based at least on the disease or disorder identified at each of the plurality of time points.

[0173] In some embodiments, selecting the one or more data analysis tools comprises receiving a user selection of the one or more data analysis tools. In some embodiments, selecting the one or more data analysis tools is automatically performed by the computer without receiving a user selection of the one or more data analysis tools.

[0174] In another aspect, the present disclosure provides a computer system for assessing a condition of a subject, comprising: a database that is configured to store a dataset of a biological sample of the subject; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to: (i) select one or more data analysis tools comprising: a BIG-C™ big data analysis tool, an I-Scope™ big data analysis tool, a T-Scope™ big data analysis tool, a CellScan big data analysis tool, an MS (Molecular Signature) Scoring™ analysis tool, a Gene Set Variation Analysis (GSVA) tool (e.g., P-Scope), a CoLTs® (Combined Lupus Treatment Scoring) analysis tool, a Target Scoring analysis tool, or a combination thereof; (ii) process the dataset using the one or more data analysis tools to generate a data signature of the biological sample of the subject; and (iii) based at least in part on the data signature generated in (ii), assess the condition of the subject.

[0175] In another aspect, the present disclosure provides a non-transitory computer readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for assessing a condition of a subject, the method comprising: (a) receiving a dataset of a biological sample of the subject; (b) selecting one or more data analysis tools, wherein the one or more data analysis tools comprise an analysis tool selected from the group consisting of : a BIG-C™ big data analysis tool, an I-Scope™ big data analysis tool, a T-Scope™ big data analysis tool, a CellScan big data analysis tool, an MS (Molecular Signature) Scoring ™ analysis tool, a Gene Set Variation Analysis (GSVA) tool (e.g., P-Scope), a CoLTs® (Combined Lupus Treatment Scoring) analysis tool, and a Target Scoring analysis tool; (c) processing the dataset using the one or more data analysis tools to generate a data signature of the biological sample of the subject; and (d) based at least in part on the data signature generated in (c), assessing the condition of the subject. In any embodiment described herein, the one or more data analysis tools may be a plurality of data analysis tools each independently selected from a BIG-C™ big data analysis tool, an I-Scope™ big data analysis tool, a T-Scope™ big data analysis tool, a CellScan big data analysis tool, an MS (Molecular Signature) Scoring ™ analysis tool, a Gene Set Variation Analysis (GSVA) tool (e.g., P-Scope), a CoLTs® (Combined Lupus Treatment Scoring) analysis tool, and a Target Scoring analysis tool.

[0176] To obtain a blood sample, various techniques may be used, e.g., a syringe or other vacuum suction device. A blood sample may be optionally pre-treated or processed prior to use. A sample, such as a blood sample, may be analyzed under any of the methods and systems herein within 4 weeks, 2 weeks, 1 week, 6 days, 5 days, 4 days, 3 days, 2 days, 1 day, 12 hr, 6 hr, 3 hr, 2 hr, or 1 hr from the time the sample is obtained, or longer if frozen. When obtaining a sample from a subject (e.g., blood sample), the amount may vary depending upon subject size and the condition being screened. In some embodiments, at least 10 mL, 5 mL, 1 mL, 0.5 mL, 250, 200, 150, 100, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 pL of a sample is obtained. In some embodiments, 1-50, 2-40, 3-30, or 4-20 pL of sample is obtained. In some embodiments, more than 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100 pL of a sample is obtained.

[0177] The sample may be taken before and / or after treatment of a subject with a disease or disorder. Samples may be obtained from a subject during a treatment or a treatment regime. Multiple samples may be obtained from a subject to monitor the effects of the treatment over time. The sample may be taken from a subject known or suspected of having a disease or disorder for which a definitive positive or negative diagnosis is not available via clinical tests. The sample may be taken from a subject suspected of having a disease or disorder. The sample may be taken from a subject experiencing unexplained symptoms, such as fatigue, nausea, weight loss, aches and pains, weakness, or bleeding. The sample may be taken from a subject having explained symptoms. The sample may be taken from a subject at risk of developing a disease or disorder due to factors such as familial history, age, hypertension or prehypertension, diabetes or pre-diabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, alcohol consumption, or drug use), or presence of other risk factors.

[0178] In some embodiments, a sample may be taken at a first time point and assayed, and then another sample may be taken at a subsequent time point and assayed. Such methods may be used, for example, for longitudinal monitoring purposes to track the development or progression of a disease. In some embodiments, the progression of a disease may be tracked before treatment, after treatment, or during the course of treatment, to determine the treatment’s effectiveness.

[0179] For example, a method as described herein may be performed on a subject prior to, and after, treatment with a first, second, and / or third disease condition therapy to measure the disease’s progression or regression in response to the first, second, and / or third disease condition therapy. The first, second, and / or third disease can be as described above.

[0180] After obtaining a sample from the subject, the sample may be processed to generate datasets indicative of a disease or disorder of the subject. For example, a presence, absence, or quantitative assessment of nucleic acid molecules of the sample from a panel of condition-associated genomic loci or nucleotide polymorphism may be indicative of first, second, and / or third disease condition of the subject. Processing the sample obtained from the subject may comprise (i) subjecting the sample to conditions that are sufficient to isolate, enrich, or extract a plurality of nucleic acid molecules, and (ii) assaying the plurality of nucleic acid molecules to generate the dataset (e.g., microarray data, nucleic acid sequences,or quantitative polymerase chain reaction (qPCR) data). Methods of assaying may include any assay known in the art or described in the literature, for example, a microarray assay, a sequencing assay (e.g., DNA sequencing, RNA sequencing, or RNA-Seq), or a quantitative polymerase chain reaction (qPCR) assay.

[0181] In some embodiments, a plurality of nucleic acid molecules is extracted from the sample and subjected to sequencing to generate a plurality of sequencing reads. The nucleic acid molecules may comprise ribonucleic acid (RNA) or deoxyribonucleic acid (DNA). The extraction method may extract all RNA or DNA molecules from a sample. Alternatively, the extraction method may selectively extract a portion of RNA or DNA molecules from a sample. Extracted RNA molecules from a sample may be converted to cDNA molecules by reverse transcription (RT).

[0182] The sample may be processed without any nucleic acid extraction. For example, the disease or disorder may be identified or monitored in the subject by using probes configured to selectively enrich nucleic acid (e.g., RNA or DNA) molecules corresponding to a panel of condition-associated genomic loci. The probes may be nucleic acid primers. The probes may have sequence complementarity with nucleic acid sequences from one or more of the panel of condition-associated genomic loci. The panel of condition-associated genomic loci may comprise at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, or more condition-associated genomic loci.

[0183] The probes may be nucleic acid molecules (e.g., RNA or DNA) having sequence complementarity with nucleic acid sequences (e.g., RNA or DNA) of one or more genomic loci (e.g., condition-associated genomic loci). These nucleic acid molecules may be primers or enrichment sequences. The assaying of the sample using probes that are selective for the one or more genomic loci (e.g., condition-associated genomic loci) may comprise use of array hybridization, polymerase chain reaction (PCR), or nucleic acid sequencing (e.g., RNA sequencing or DNA sequencing, such as RNA-Seq).

[0184] The assay readouts may be quantified at one or more genomic loci (e.g., condition-associated genomic loci) to generate the data indicative of the disease or disorder. For example, quantification of array hybridization or polymerase chain reaction (PCR) corresponding to a plurality of genomic loci (e.g., condition-associated genomic loci) may generate data indicative of the disease or disorder. Assay readouts may comprise quantitative PCR (qPCR) values, digital PCR (dPCR) values, digital droplet PCR (ddPCR) values, fluorescence values, etc., or normalized values thereof.

[0185] The BIG-C (Biologically Informed Gene Clustering) tool may be configured to sort large groups of genes into a set of functional groups (e.g., 53 functional groups). The functional groups are created utilizing publicly available information from online tools and databases including UniProtKB / Swiss-Prot, GO Terms, KEGG pathways, NCBI PubMed, and the Interactome. The functional groups may includeone or more of: Active RNA, Anti-apoptosis, anti-proliferation, autophagy, chromatin remodeling, cytoplasm and biochemistry, cytoskeleton, DNA repair, endocytosis, endoplasmic reticulum, endosome and vesicles, fatty acid biosynthesis, cell surface, transcription, glycolysis and gluconeogenesis, golgi, immune cell surface, immune secreted, immune signaling, integrin pathway, interferon stimulated genes, intracellular signaling, lysosome, melanosome, MHC class I, MHC class II, microRNA processing, microRNA, mitochondrial transcription, mitochondria, mitochondria oxidative phosphorylation, mitochondrial TCA cycle, mRNA processing, mRNA splicing, non-coding RNA, nuclear receptor, nucleus and nucleolus, palmitoylation, pattern recognition receptors, peroxisomes, pro-apoptosis, pro-cell cycle, proteasome, pseudogenes, RAS superfamily, reactive oxygen species protection, secreted and extracellular matrix, transcription factors, transporters, transposon control, ubiquitylation and sumoylation, unfolded protein and stress, and unknown. Enrichment scores for each group are calculated based on an overlap p value to determine the functional groups over or under-expressed in the gene expression dataset. The BIG-C may be configured such that each gene is sorted into only one of the 53 functional groups, allowing for a quick and relatively simple understanding of types of genes enriched and co-expressed in a big dataset.

[0186] The I-Scope™ tool may be configured to identify immune infiltrates. Hematopoietic cells are unique in that they move throughout the body patrolling for threats to the host, and may infiltrate tissue sites not normally home to immune cells. I-Scope™ may be configured to identify hematopoietic cells through an iterative search of more than 17,000 genes identified in more than 50 microarray datasets. From this search, 1226 candidate genes are identified and researched for restriction in hematopoietic cells as determined by the HPA, GTEx and FANTOM5 datasets (e.g., available at proteinatlas.org). 926 genes meet the criteria for being mainly restricted to hematopoietic lineages (brain, reproductive organ exclusions were permitted). These genes are researched for immune cell specific expression in 27 hematopoietic sub-categories: alpha beta T cell, T cell, regulatory T Cell, activated T cell, anergic T cell, gamma delta T cells, CD8 T, NK / NKT cell, NK cell, T & B cells, B cells, germinal center B cells, B cell and plasmacytoid dendritic cell, T &B & myeloid, B & myeloid, T & myeloid, MHC Class II expressing cell, monocyte, dendritic cell, plasmacytoid dendritic cells, myeloid cell, plasma cell, erythrocyte, neutrophil, low density granulocyte, granulocyte, and platelet. Transcripts are entered into I-Scope™ and the number of transcripts in each category determined. Odd’s ratios are calculated with confidence intervals using the Fisher’s exact test in R.

[0187] The T-Scope™ tool may be configured to help identify types of non-hematopoietic cells in gene expression datasets. T-Scope™ may be configured by downloading approximately 10,000 tissue enriched and 8,000 cell line enriched genes from the human protein atlas along with their tissue or cell line designation (e.g., available at proteinatlas.org). Genes found in more than four tissues are eliminated. Housekeeping genes described in the gene expression study by She et al. are also removed (e.g., as described by She et al., “Definition, conservation and epigenetics of housekeeping and tissue-enriched genes,” BMC Genomics 2009, 10:269, which is incorporated herein by reference in its entirety). This list is further curated by removing genes differentially expressed in 34 hematopoietic cell gene expressiondatasets and adding kidney specific genes from datasets downloaded from the GEO repository and processed by Ampel BioSolutions. The resulting categories of genes represent genes enriched in the following 42 tissue / cell specific categories: adrenal gland, breast, cartilage, cerebral cortex, uterine cervix, chondrocyte, colon, duodenum, endometrium, epididymis, esophagus fallopian tube, esophagus, fibroblast, heart muscle, keratinocyte, kidney, liver, lung, melanocyte, ovary pancreas, parathyroid gland, placenta, podocyte, prostrate, rectum, salivary gland, seminal vesicle, skeletal muscle, skin, small intestine, smooth muscle, stomach, synoviocyte, testis, kidney loop of henle, kidney proximal tubule, kidney distal tubule, and kidney collecting duct.

[0188] The CellScan tool may be a combination of I-Scope™ and T-Scope™ , and may be configured to analyse tissues with suspected immune infiltrations that may also have tissue specific genes. CellScan may potentially be more stringent than either I-Scope™ or T-Scope™ because it may be used to distinguish resident tissue cells from non-resident hematopoietic cells.

[0189] The MS (Molecular Signature) Scoring tool may be configured to assess specific pathways in a disease state. Information on genes that encode for proteins that participate in a specific signaling pathway, and whether the gene product promotes or inhibits the pathway, are compiled and curated through literature mining. Curated pathways presented by the company include CD40-CD401igand, IL-6, IL- 12 / 23, TNT, IL- 17, IL-21, S IP I, IL- 13 and PDE4, but this method may be used for any known signaling pathway with available data. To determine if a signaling pathway is over or under-expressed in a microarray dataset, the gene list for each signaling pathway may be queried against the limma differentially expressed genes from a disease state compared to healthy controls, and the differentially expressed genes in the signaling pathway may be identified for each set. The fold changes for genes that promoted the pathway may be added together and the fold changes for genes that inhibited the pathway may be subtracted from the score. This total score may be normalized based on the number of genes that may be detected on the specific microarray platform used for the experiment. Activation scores of -100 to +100 may be determined using this method with negative scores indicating an inhibition of the specific pathway in the disease state and positive scores indicating an up-regulation of a specific pathway in the disease state. The Lischer’s exact test may be performed to determine if there was sufficient overlap of genes between the experimental differentially expressed genes and the genes in the signaling pathway.

[0190] Gene Set Variation Analysis (GSVA) may be performed (for example, as described in Catalina et al. (2019, Communications Biology, “Gene expression analysis delineates the potential roles of multiple interferons in systemic lupus erythematosus”, which is incorporated herein by reference in its entirety) to determine enrichment of signaling pathways in individual patient samples. Gene set variation analysis may be performed using an open source software package for the coding language R available at the R Bioconductor (bioconductor.org), e.g., as described by Hanzelman et al., (“GSVA: gene set variation analysis for microarray and RNA-Seq data,” BMC Bioinformatics, 2013, which is incorporated herein by reference in its entirety). The modules of genes to interrogate the datasets may be developed. Modules of genes determined to represent a specific signaling pathway or process may be identified (e.g., usingpublicly available datasets). For example, the IFNB1 signaling pathway is taken from a publicly available gene expression dataset of peripheral blood cells treated with IFNB1 in vitro. Genes co-expressed in this dataset (genes either all increased or decreased compared to control treated peripheral blood) are used to create modules of genes representing the IFNB 1 signaling pathway, and GSVA is used to determine the enrichment of this set of genes and hence the IFNB1 signaling pathway in individual patient and control samples.

[0191] The CoLTs®, or Combined Lupus Treatment Scoring, may be configured to rank identified drugs or therapies by a number of essential characteristics, including scientific rationale, experience in lupus mice / human cells (preclinical), previous clinical experience in autoimmunity, drug properties, and safety profile, including adverse events. Face and test validities may be established by scoring SOC medications and confirming the scores with a panel of lupus clinicians. The final result may be the CoLTs® score. A CoLTs® algorithm may also be configured for drugs in development (DID), which typically do not have drug metabolism and adverse event information available.

[0192] The target scoring algorithm may be configured to prioritize a specific gene or protein that is potentially a good choice to target with a drug in first, second and / or third disease patients. It may be utilized even if there is currently no drug available to the target gene or protein. The algorithm may be based on the addition of 18 data based determinations plus the overall scientific rationale and generates scores from -13 (not a good target in SLE) to 27 (very promising target in SLE).BIG-C™ big data analysis tool

[0193] BIG-C® is a fast and efficient cloud-based tool to functionally categorize gene products. With coverage of over 80% of the genome, BIG-C® leverages publicly available databases such as UniProtKB / Swiss-Prot, GO terms, KEGG pathways, NCBI PubMed and Interactome to place genes into 53 functional categories. The sorting into only one of 53 functional groups allows for a quick and relatively simple understanding of types of genes enriched and co-expressed in a big dataset. This assists in deriving further insights from genes expressed for a given disease state in human or pre-clinical mouse models.

[0194] BIG-C® may be used to functionally categorize immunological genes that are not covered in cancer databases such as GO and KEGG (e.g., as described by Grammer et al. 2016, “Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis,” Lupus, 25(10), 1150-1170, which is incorporated herein by reference in its entirety). Using a knowledge base of over 5000 patients with systemic lupus erythematosus (SLE), over 16432 genes are each placed into one of 53 BIG-C® functional categories, and statistical analysis is performed to identify enriched categories. BIG-C® categories are cross-examined with the GO and KEGG terms to obtain additional information and insights.

[0195] A sample BIG-C® workflow may comprise the following steps. First, SLE genomic datasets arederived from whole blood, peripheral blood mononuclear cells, affected tissues, and purified immune cells. Second, datasets are analyzed using DE analysis (as shown by a differential expression heatmap) orWeighted Gene Coexpression Network Analysis (WGCNA) (as shown by a gene coexpression plot). Third, expressed genes are annotated using publicly available databases (e.g., UniProtKB / Swiss-Prot database, Human Immunodeficiencies database, Mouse MGI database, Entrez Molecular Sequence database, PubMed, and the Human Tissue Atlas). Fourth, signatures are cross-referenced with purified single-cell microarray datasets and RNAseq experiments. Fifth, BIG-C® is leveraged to separate the individual annotated genes into one of 53 functional categories (e.g., as described by Labonte et al. 2018, “Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus,” PloS one, 13(12), e0208132, which is incorporated herein by reference in its entirety). Sixth, chi-squared analysis is used to determine enriched categories of interest from overlap p-values. Seventh, enriched categories are cross-examined with GO and KEGG terms to derive key insights for further analysis.I-Scope™ big data analysis tool

[0196] I-Scope™ may be a tool configured for cross-examining the presence and activity of varying types of immune cell infiltrates with observed gene expression patterns. It may take annotated gene expression data and analyze it for hematopoietic cell lineage. I-Scope™ may be used downstream of the BIG-C® (Biologically Informed Gene-Clustering) tool in that it helps to provide even more insight into the nature of the genes being expressed after categorization.

[0197] I-Scope™ addresses the need to understand the involvement of specific cells for a given disease state. While it is helpful to understand the relative up-regulation and down-regulation at the gene expression level, it is even more informative to understand specifically in which cells this is occurring. I- Scope™ may be configured to identify hematopoietic cells through an iterative search of more than 17,000 genes identified in more than 50 microarray datasets (e.g., as described by Hubbard et al., “Analysis of Lupus Synovitis Gene Expression Reveals Dysregulation of Pathogenic Pathways Activated within Infiltrating Immune Cells,” Arthritis Rheumatol, 2018; 70 (suppl 10), which is incorporated herein by reference in its entirety). I-Scope™ may function by restricting the analysis to genes of hematopoietic cell heritage and allow for cross-checking against purified single-cell experiments or datasets. The crosscheck confirms and categorizes specific transcript signatures to the 28 hematopoietic cell sub-categories, ultimately allowing for cellular activity analysis across multiple samples and disease states. When combined with BIG-C® categories, the cellular activity may be correlated to specific functions within a given cell type.

[0198] A sample I-Scope™ workflow may comprise the following steps. First, candidate genes are identified from SLE (systemic lupus erythematosus) datasets potentially associated with immune cell expression. Second, using HPA, GTEx, and FANTOM5 datasets, expression signatures associated with hematopoietic cell lineage are identified. Third, signatures are cross-referenced with purified single-cell microarray datasets and RNAseq experiments. Fourth, transcripts are categorized into 28 hematopoietic cell sub-categories and assess cellular expression across different samples and disease states. Odd’s ratios are calculated with confidence intervals using the Fisher’s exact test in R. An I-Scope™ signatureanalysis for a given sample may lead to the I-Scope™ signature analysis across multiple samples and disease states.T-Scope™ big data analysis tool

[0199] The T-Scope™ tool may be configured for cross-examining gene expression signatures of a given sample with a database of non-hematopoietic cell types (e.g., as described by Hubbard et al., “Analysis of Gene Expression from Systemic Lupus Erythematosus Synovium Reveals Unique Pathogenic Mechanisms [Abstract], Annual Meeting of the American College of Rheumatology; June 2019; Chicago, IL, which is incorporated herein by reference in its entirety). T-Scope™ may comprise a database of 704 transcripts allocated to 45 independent categories. Transcripts detected in the sample are matched to one of the cellular categories within the T-Scope™ tool to derive further insights on tissue cell activity. T- Scope™ may be used downstream of the BIG-C® (Biologically Informed Gene -Clustering) tool to understand which tissue cell types are present. In conjunction with I-Scope™ (which provides information related to immune cells), T-Scope™ may be performed to provide a complete view of all possible cell activity in a given sample.

[0200] T-Scope™ addresses the need to understand the involvement of specific tissue cells for a given disease state. While it is helpful to understand the relative up-regulation and down-regulation at the gene expression level, it is even more informative to understand specifically in which cells this is occurring. T- Scope™ may be configured by downloading a set of approximately 10,000 tissue enriched and 8,000 cell line enriched genes from the Human Protein Atlas along with their tissue or cell line designation. Genes differentially expressed in hematopoietic cell datasets are removed and kidney specific genes are added from the GEO repository. T-Scope™ may function by restricting the analysis to genes of known tissue cell heritage and allow for cross-checking against purified single-cell experiments or datasets. The crosscheck confirms and categorizes specific transcript signatures to the 45 tissue cell sub-categories, ultimately allowing for cellular activity analysis across multiple samples and disease states. When combined with BIG-C® categories, the cellular activity may be correlated to specific functions within a given tissue cell type.

[0201] A sample T-Scope™ workflow may comprise the following steps. First, candidate genes are identified from SLE (systemic lupus erythematosus) differential expression datasets potentially associated with tissue cell expression. Second, using publicly available databases, expression signatures associated with potential tissue cell activity are identified. Third, signatures are cross-referenced with microarray, scRNAseq or RNAseq experiments. Fourth, transcripts are categorized into 45 tissue cell sub-categories and cellular expression is assessed across different samples and disease states. Results may be obtained using T-Scope™ in combination with I-Scope™ for identification of cells post-DE-analysis.CellScan big data analysis tool

[0202] A cloud-based genomic platform may be configured to provide users with access to CellScan™, which comprises a suite of tools for the identification, analysis, and prioritization of targets for drugdevelopment and / or repositioning. This platform is powered by a database containing the genomic information gathered from 5000+ autoimmune patients. The cloud-based genomic platform may leverage results from RNAseq and microarray experiments in conjunction with clinical information, such as medication and lab tests, to provide undiscovered insights.

[0203] CellScan™ may go beyond typical ‘omics analysis by performing one or more of the following: functionally categorizing genes and their products (e.g., using BIG-C®); deconvolving gene expression data to identify unique immunological cell types from blood or biopsy samples (e.g., using I-Scope™); identifying tissue specific cell from biopsy samples (e.g., using T-Scope™); identifying receptor-ligand interactions and subsequent signaling pathways (e.g., using MS-Scoring™); ranking genes and their products for targeting by drugs and miRNA mimetics (e.g., using Target-Scoring™); and prioritizing FDA-approved drugs and drugs-in-development for treatment in patients or pre-clinical models (e.g., using CoLTs®).

[0204] CellScan™ applications may include one or more of: Biomarker Discovery, Disease Mechanisms, Drug Mechanism of Action, Drug Mechanism of Toxicity, and Target Identification and Validation. Experimental approaches supported by CellScan™ may include one or more of: IncRNA, Metabolomics, MicroArray, miRNA, mRNA, qPCR, Proteomics, and RNAseq.

[0205] Data analysis and interpretation with CellScan™ may build on comprehensive, manually curated content of a knowledge base. Powerful, quick, and efficient tools may be used to perform deep analysis of NGS and miRNA data to identify gene function, immunological and tissue cell type, pathways, and target / drug appropriate for a specific disease state.

[0206] CellScan™ features may be configured to optimize or maximize the impact of information that surfaces in an analysis so that interpretation of a dataset is comprehensive and elucidates actionable insights. These features may include one or more of: NGS RNAseq data analysis, biomarker scoring, and prioritizing targets and drugs for human clinical trials and / or pre-clinical models. The NGS RNAseq data analysis may comprise interrogating RNA and miRNA data for function, cell-type (immunological or tissue) and pathways. The biomarker scoring may comprise using a knowledge base and gene expression data to assess and prioritize biomarkers associated with a target disease or phenotype. The target / drug prioritization may comprise leveraging objective scoring of targets and drugs based on parameters such as scientific rationale, evidence in mouse / human cells, prior clinical data, overall drug properties, and the risk of adverse events.

[0207] The knowledge base may be a repository created from millions of individual pieces of information gathered about genes, cells, tissues, drugs, and diseases, and manually reviewed for accuracy and includes rich contextual details and links to original publications. The knowledge base may enable access to relevant and substantiated knowledge from primary literature as well as public and private databases for comprehensive interpretation of NGS / RNAseq data elucidating function / pathways and prioritize targets / drugs for given disease states. An example list of reference databases for the content in CellScan™, with both human and mouse species-specific identifiers supported.MS (Molecular Signature) Scoring™ analysis tool

[0208] MS-Scoring™ may be configured to identify receptor-ligand interactions and predict ongoing signaling pathways. In addition, MS-Scoring™ may be used to validate molecular pathways as potential targets for new or repurposed drug therapies. The specificity of next-generation drug therapies requires a way to understand the potential of a given therapy to act on the intended biochemical target. Moreover, a potential application of this is the repositioning of drug therapies that may have the correct biochemical targeting to address multiple clinical needs beyond the initial intended therapeutic value.

[0209] MS-Scoring™ may be specifically developed to address gaps in the QIAGEN IPA® (Ingenuity Pathway Analysis) tool that does not contain many immunologically relevant pathways. Similar to IPA®, MS-Scoring™ 1 may use log-fold change information to score the target and its signaling pathway to verify the viability of the targets. If the fold-change of the genes of a signaling pathway appears to be upregulated or inhibitors appear to be downregulated, MS-Scoring™ 1 may provide a score of +1. Conversely if the genes of a signaling pathway appear downregulated or the inhibitors upregulated, MS- Scoring™ 1 may provide a score of -1. A score of zero may be provided if no fold-change is observed. The scores may then be summed and normalized across the entire pathway to yield a final %score between -100 (inhibition) and +100 (up-regulation). Higher absolute magnitude scores, scores that are close to -100 or +100, may indicate a high potential for therapeutic targeting. The Fischer’s exact test may be performed to determine if there is sufficient overlap of genes between the experimental differentially expressed genes and the genes in the signaling pathway.

[0210] A sample MS-Scoring™ 1 workflow may comprise the following steps. First, potential drugs and pathways are identified by LINCS (Library of Integrated Network-Based Cellular Signatures) as candidates for therapeutic intervention. Second, MS-Scoring™ 1 is used to evaluate individual transcript elements of the target pathway. Third, signatures are cross-referenced with purified single-cell microarray datasets and RNAseq experiments. Fourth, scores are compiled and normalized to provide an overall % score for the pathway and higher absolute magnitude scores indicate a higher potential for therapeutic targeting.

[0211] MS-Scoring™ 1 may be performed of IL-12 and IL-23 related pathways for targeting using ustekinumab for SLE (systemic lupus erythematosus) drug repositioning (e.g., as described by Grammer et al., 2016, “Drug repositioning in SLE: crowd-sourcing, literature -mining and Big Data analysis,” Lupus, 25(10), 1150-1170, which is incorporated herein by reference in its entirety).

[0212] MS-Scoring™ 2 may utilize custom-defined gene modules that represent a signaling pathway or process and is particularly useful for gene expression datasets from microarray or RNAseq. The MS- Scoring™ 2 tool may be configured to take a deeper look at signaling pathways analyzed using the MS- Scoring™ 1. The tool may analyze raw gene expression data and assess enrichment by the Gene Set Variation Analysis (as described herein), which assigns an indexed score to the individual co-expressed pathways between -1 and +1 indicating levels of down-regulation and up-regulation respectively.

[0213] A sample MS-Scoring™ 2 workflow may comprise the following steps. First, a signaling pathway of interest is selected from the MS-Scoring™ 2 menu. Second, a raw gene expression data is inputted into the MS-Scoring™ 2 tool. Third, enrichment of signaling pathway(s) is assessed on a patient by patient basis. Fourth, the data may then be used to drive insight for the target signaling pathways in individual patient samples.

[0214] Results from GSVA Analysis on SLE (systemic lupus erythematosus) signaling pathways may be, e.g., as described by Hanzelmann et al., “GSVA: Gene Set Variation Analysis for Microarray and RNA- Seq Data,” BMC Bioinformatics, vol. 14, no. 1, 2013, p. 7., which is incorporated herein by reference in its entirety.CoLTs®(Combined Lupus Treatment Scoring) analysis tool

[0215] A scoring method called CoLTs®, or Combined Lupus Treatment Scoring, may be configured to assessing and prioritizing the repositioning potential of drug therapies. CoLTs® may rank identified drugs / therapies by a number of essential characteristics, including scientific rationale, experience in lupus mice / human cells (preclinical), previous clinical experience in autoimmunity, drug properties, and safety profile, including adverse events. Face and test validities may be established by scoring standard of care (SOC) medications and confirming the scores with a panel of lupus clinicians. The final result may be the CoLTs® score. A CoLTs® algorithm may also be configured for drugs in development (DID) since they typically do not have drug metabolism and adverse event information available.

[0216] CoLTs® may be configured to perform objective scoring of drug molecules based on a hypothesis-based literature search of publicly available databases. The tool has the ability to rank drug molecules from both FDA-approved and non-approved classes and ranked based upon parameters such as scientific rationale, evidence in mouse / human cells, prior clinical data, overall drug properties, and the risk of adverse events. The parameters are used within five independent drug therapy categories: small molecules, biologies, complementary and alternative therapies, and drugs in development.

[0217] CoLTs® may address the need for a systematic and objective way to evaluate the potential of drug therapies to be repositioned for treatment of autoimmune diseases, initially within SLE (systemic lupus erythematosus). The composite score may embody all the accessible information in literature databases, inclusive of efficacy and adverse reactions, to be able to assist in the prioritization of drug development. While the composite score takes into account many aspects of a drug, it may heavily weigh the risk of adverse events and ranges from -16 to +11. CoLT Scoring® may be validated through repeated scoring of 215 potential therapies using a total of over 5000 reference data points as well as by clinicians specializing in the field of rheumatology. Specifically, CoLTs®’ prediction of Stelara / Ustekinumab to be a top priority biologic for lupus drug repositioning is validated by a successful Phase 2 clinical trial (e.g., as described by Vollenhoven et al., “Efficacy and Safety of Ustekinumab, an IL- 12 and IL-23 Inhibitor, in Patients with Active Systemic Lupus Erythematosus: Results of a Multicentre, Double-Blind, Phase 2, Randomised, Controlled Study.” The Lancet, vol. 392, no. 10155, 2018, pp. 1330-1339, which isincorporated herein by reference in its entirety). CoLTs® may be calibrated on SoC (Standard of Care) therapies for the individual autoimmune disease being assessed.

[0218] Within the ten major categories, rationale ranges from 0 to +3, mouse / human in vitro experience ranges from -1 to +1, clinical properties are on a scale of -3 to +3, the adverse effect of inducing lupus ranges from -1 to 0, metabolic properties range from -2 to 0, and finally adverse events (such as toxicity, infection, carcinogenic, etc.) were given a score of -5 to 0 (e.g., as described by Grammer et al., 2016, “Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis,” Lupus, 25(10), 1150-1170, which is incorporated herein by reference in its entirety). For example, CoLT Scoring® of SOC Therapies in Lupus (Belimumab, HCQ, and Rituximab) may be performed.Target Scoring analysis tool

[0219] The Target scoring algorithm may be configured to prioritize a specific gene or protein that would potentially be a good choice to target with a drug in lupus patients. It may be utilized even if there is currently no drug available to the target gene or protein. The algorithm may be based on the addition of 18 data based determinations plus the overall scientific rationale and generates scores from -13 (not a good target in SLE) to 27 (very promising target in SLE).

[0220] Target-Scoring™ may be configured to assessing and prioritizing the potential of molecular targets for further development of drug therapies. The Target-Scoring™ tool is very similar to CoLTs® except it approaches the need for new SLE therapies from a different angle. Target Scoring may be configured to perform an objective assessment of molecular targets for the development of new or repurposed drug therapies. Like CoLTs®, it also derives data from a hypothesis-based literature search and generates a composite score based on the publicly available information. Leveraging the composite score, researchers may better prioritize the development of novel drug therapies addressing the assessed targets of interest.

[0221] Target-Scoring™ may utilize 19 different scoring categories to derive a composite score that ranges from -13 to +27 for the suitability of a gene target for SLE therapy development. Target-Scoring™ may be validated through repeated scoring of potential therapies as well as by clinicians (e.g., clinicians specializing in the field of immunology).Classifiers

[0222] In some embodiments, the present disclosure provides a system, method, or kit having data analysis realized in software application, computing hardware, or both. In various embodiments, the analysis application or system includes at least a data receiving module, a data pre-processing module, a data analysis module, a data interpretation module, or a data visualization module. In one embodiment, the data receiving module may comprise computer systems that connect laboratory hardware or instrumentation with computer systems that process laboratory data. In one embodiment, the data preprocessing module may comprise hardware systems or computer software that performs operations on the data in preparation for analysis. Examples of operations that may be applied to the data in the pre-processing module include affine transformations, denoising operations, data cleaning, reformatting, or subsampling. A data analysis module, which may be specialized for analyzing genomic data from one or more genomic materials, can, for example, take assembled genomic sequences and perform probabilistic and statistical analysis to identify abnormal patterns related to a disease, pathology, state, risk, condition, or phenotype. A data interpretation module may use analysis methods, for example, drawn from statistics, mathematics, or biology, to support understanding of the relation between the identified abnormal patterns and health conditions, functional states, prognoses, or risks. A data visualization module may use methods of mathematical modeling, computer graphics, or rendering to create visual representations of data that may facilitate the understanding or interpretation of results.

[0223] Feature sets may be generated from datasets obtained using one or more assays of a biological sample obtained or derived from a subject, and a trained algorithm may be used to process one or more of the feature sets to identify or assess a condition (e.g., a disease or disorder, such as first, second, and / or third disease condition) of a subject. For example, the trained algorithm may be used to apply a machine learning classifier to a plurality of condition-associated genomic loci that are associated with two or more classes of individuals inputted into a machine learning model, in order to classify a subject into one of the two or more classes of individuals. For example, the trained algorithm may be used to apply a machine learning classifier to a plurality of condition-associated that are associated with individuals with known conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) and individuals not having the condition (e.g., healthy individuals, or individuals who do not have first, second, and / or third disease condition), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome).

[0224] The trained algorithm may be configured to identify the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than 99%. This accuracy may be achieved for a set of at least about 25, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, at least about 1,000, or more than about 1,000 independent samples.

[0225] The trained algorithm may comprise a machine learning algorithm, such as a supervised machine learning algorithm. The supervised machine learning algorithm may comprise, for example, a Random Forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may comprise a classification and regression tree (CART) algorithm. The trained algorithm may comprise an unsupervised machine learning algorithm.

[0226] The trained algorithm may comprise a classifier configured to accept as input a plurality of input variables or features (e.g., condition-associated genomic loci) and to produce or output one or more outputvalues based on the plurality of input variables or features (e.g., condition-associated genomic loci). The plurality of input variables or features may comprise one or more datasets indicative of the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition). For example, an input variable or feature may comprise a number of sequences corresponding to or aligning to each of the plurality of condition- associated genomic loci.

[0227] The plurality of input variables or features may also include clinical information of a subject, such as health data. For example, the health data of a subject may comprise one or more of: a diagnosis of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), a prognosis of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), a risk of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), a treatment history of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), a history of previous treatment for one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), a history of prescribed medications, a history of prescribed medical devices, age, height, weight, sex, smoking status, and one or more symptoms of the subject.

[0228] For example, the disease or disorder may comprise one or more of: lupus, coronary artery disease (CAD), myocardial infraction, ischemic stroke, coronary atherosclerosis, cardiomyopathy, depression, asthma, chronic obstructive pulmonary disease (COPD), diabetes mellitus, nonalcoholic fatty liver disease, metabolic disorder inflammatory bowel disease, or glomerulonephritis. As another example, the symptoms may include one or more of: alopecia, anti-dsDNA seropositivity, arthritis, fever, hematuria, leukopenia, low serum complement, mucosal ulcer, myositis, pericarditis, pleurisy, proteinuria, pyuria, rash, thrombocytopenia, urinary cast, vasculitis, visual disturbance, or a combination thereof. As another example, the prescribed medications or drugs may include one or more of: antimalarials, corticosteroids, immunosuppressants, and nonsteroidal anti-inflammatory drugs (NSAIDs).

[0229] The trained algorithm may comprise a classifier, such that each of the one or more output values comprises one of a fixed number of possible values (e.g., a linear classifier, a logistic regression classifier, etc.) indicating a classification of the sample by the classifier. The trained algorithm may comprise a binary classifier, such that each of the one or more output values comprises one of two values (e.g., {0, 1}, {positive, negative}, or {high-risk, low-risk}) indicating a classification of the sample by the classifier. The trained algorithm may be another type of classifier, such that each of the one or more output values comprises one of more than two values (e.g., {0, 1, 2}, {positive, negative, or indeterminate}, or {high-risk, intermediate-risk, or low-risk}) indicating a classification of the sample by the classifier.

[0230] The classifier may be configured to classify samples by assigning output values, which may comprise descriptive labels, numerical values, or a combination thereof. Some of the output values may comprise descriptive labels. Such descriptive labels may provide an identification or indication of thepresence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of the subject, and may comprise, for example, positive, negative, high-risk, intermediate-risk, low-risk, or indeterminate. Such descriptive labels may provide an identification of a treatment for the one or more conditions of the subject, and may comprise, for example, a therapeutic intervention, a duration of the therapeutic intervention, and / or a dosage of the therapeutic intervention suitable to treat the one or more conditions of the subject. Such descriptive labels may provide an identification of secondary clinical tests that may be appropriate to perform on the subject, and may comprise, for example, an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof. For example, such descriptive labels may provide a prognosis of the one or more conditions of the subject. As another example, such descriptive labels may provide a relative assessment of the one or more conditions of the subject. Some descriptive labels may be mapped to numerical values, for example, by mapping “positive” to 1 and “negative” to 0.

[0231] The classifier may be configured to classify samples by assigning output values that comprise numerical values, such as binary, integer, or continuous values. Such binary output values may comprise, for example, {0, 1}, {positive, negative}, or {high-risk, low-risk}. Such integer output values may comprise, for example, {0, 1, 2}. Such continuous output values may comprise, for example, a probability value of at least 0 and no more than 1. Such continuous output values may comprise, for example, an unnormalized probability value of at least 0. Such continuous output values may indicate a prognosis of the one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of the subject. Some numerical values may be mapped to descriptive labels, for example, by mapping 1 to “positive” and 0 to “negative.”

[0232] The classifier may be configured to classify samples by assigning output values based on one or more cutoff values. For example, a binary classification of samples may assign an output value of “positive” or 1 if the sample indicates that the subject has at least a 50% probability of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), thereby assigning the subject to a class of individuals receiving a positive test result. As another example, a binary classification of samples may assign an output value of “negative” or 0 if the sample indicates that the subject has less than a 50% probability of having one or more conditions (e.g., a disease or disorder), thereby assigning the subject to a class of individuals receiving a negative test result. In this case, a single cutoff value of 50% is used to classify samples into one of the two possible binary output values or classes of individuals (e.g., those receiving a positive test result and those receiving a negative test result). Examples of single cutoff values may include about 1%, about 2%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, and about 99%.

[0233] As another example, the classifier may be configured to classify samples by assigning an output value of “positive” or 1 if the sample indicates that the subject has a probability of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The classification of samples may assign an output value of “positive” or 1 if the sample indicates that the subject has a probability of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of more than about 50%, more than about 55%, more than about 60%, more than about 65%, more than about 70%, more than about 75%, more than about 80%, more than about 85%, more than about 90%, more than about 91%, more than about 92%, more than about 93%, more than about 94%, more than about 95%, more than about 96%, more than about 97%, more than about 98%, or more than about 99%.

[0234] The classifier may be configured to classify samples by assigning an output value of “negative” or 0 if the sample indicates that the subject has a probability of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of less than about 50%, less than about 45%, less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 9%, less than about 8%, less than about 7%, less than about 6%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, or less than about 1%. The classification of samples may assign an output value of “negative” or 0 if the sample indicates that the subject has a probability of having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) of no more than about 50%, no more than about 45%, no more than about 40%, no more than about 35%, no more than about 30%, no more than about 25%, no more than about 20%, no more than about 15%, no more than about 10%, no more than about 9%, no more than about 8%, no more than about 7%, no more than about 6%, no more than about 5%, no more than about 4%, no more than about 3%, no more than about 2%, or no more than about 1%.

[0235] The classifier may be configured to classify samples by assigning an output value of “indeterminate” or 2 if the sample is not classified as “positive”, “negative”, 1, or 0. In this case, a set of two cutoff values is used to classify samples into one of the three possible output values or classes of individuals (e.g., corresponding to outcome groups of individuals having “low risk,” “intermediate risk,” and “high risk” of having one or more conditions, such as a disease or disorder). Examples of sets of cutoff values may include { 1%, 99%}, {2%, 98%}, {5%, 95%}, { 10%, 90%}, { 15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%}, and {45%, 55%}. Similarly, sets ofn cutoff values may be used to classify samples into one of n+1 possible output values or classes of individuals, where n is any positive integer.

[0236] The trained algorithm may be trained with a plurality of independent training samples. Each of the independent training samples may comprise a sample from a subject, associated datasets obtained byassaying the sample (as described elsewhere herein), and one or more known output values or classes of individuals corresponding to the sample (e.g., a clinical diagnosis, prognosis, absence, or treatment efficacy of a condition of the subject). Independent training samples may comprise samples and associated datasets and outputs obtained or derived from a plurality of different subjects. Independent training samples may comprise samples and associated datasets and outputs obtained at a plurality of different time points from the same subject (e.g., on a regular basis such as weekly, biweekly, or monthly), as part of a longitudinal monitoring of a subject before, during, and after a course of treatment for one or more conditions of the subject. Independent training samples may be associated with presence of the condition (e.g., training samples comprising samples and associated datasets and outputs obtained or derived from a plurality of subjects known to have the condition). Independent training samples may be associated with absence of the condition (e.g., training samples comprising samples and associated datasets and outputs obtained or derived from a plurality of subjects who are known to not have a previous diagnosis of the condition or who have received a negative test result for the condition).

[0237] The trained algorithm may be trained with at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The independent training samples may comprise samples associated with presence of the condition and / or samples associated with absence of the condition. The trained algorithm may be trained with no more than about 500, no more than about 450, no more than about 400, no more than about 350, no more than about 300, no more than about 250, no more than about 200, no more than about 150, no more than about 100, or no more than about 50 independent training samples associated with presence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition). The trained algorithm may be trained with no more than about 500, no more than about 450, no more than about 400, no more than about 350, no more than about 300, no more than about 250, no more than about 200, no more than about 150, no more than about 100, or no more than about 50 independent training samples associated with absence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition). In some embodiments, the sample is independent of samples used to train the trained algorithm.

[0238] The trained algorithm may be trained with a first number of independent training samples associated with a presence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition) and a second number of independent training samples associated with an absence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition). The first number of independent training samples associated with presence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition) may be no more than the second number of independent training samples associated with absence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition). The first number of independent training samples associated with a presence of the condition (e.g., a disease or disorder) may be equal to the second number ofindependent training samples associated with an absence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition). The first number of independent training samples associated with a presence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition) may be greater than the second number of independent training samples associated with an absence of the condition (e.g., a disease or disorder, such as first, second, and / or third disease condition).

[0239] The trained algorithm may comprise a classifier configured to identify the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) at an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more; for at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The accuracy of identifying the presence (e.g., positive test result) or absence (e.g., negative test result) of the one or more conditions by the trained algorithm may be calculated as the percentage of independent test samples (e.g., subjects known to have the condition or subjects with negative clinical test results for the condition) that are correctly identified or classified as having or not having the condition.

[0240] The trained algorithm may comprise a classifier configured to identify one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The PPV of identifying the condition using the trained algorithm may be calculated as the percentage of samples identified or classified as having the condition that correspond to subjects that truly have the condition.

[0241] The trained algorithm may comprise a classifier configured to identify one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The NPV of identifying the condition using the trained algorithm may be calculated as the percentage of samples identified or classified as not having the condition that correspond to subjects that truly do not have the condition.

[0242] The trained algorithm may comprise a classifier configured to identify one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) with a clinical sensitivity at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical sensitivity of identifying the condition using the trained algorithm may be calculated as the percentage of independent test samples associated with presence of the condition (e.g., subjects known to have the condition) that are correctly identified or classified as having the condition.

[0243] The trained algorithm may comprise a classifier configured to identify one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition) with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical specificity of identifying the condition using the trained algorithm may be calculated as the percentage of independent test samples associated with absence of the condition (e.g., subjects with negative clinical test results for the condition) that are correctly identified or classified as not having the condition.

[0244] The trained algorithm may comprise a classifier configured to identify the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder,such as first, second, and / or third disease condition) with an Area-Under-Curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more. The AUC may be calculated as an integral of the Receiver Operator Characteristic (ROC) curve (e.g., the area under the ROC curve) associated with the trained algorithm in classifying samples as having or not having the condition.

[0245] Classifiers of the trained algorithm may be adjusted or tuned to improve or optimize one or more performance metrics, such as accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof (e.g., a performance index incorporating a plurality of such performance metrics, such as by calculating a weight sum therefrom), of identifying the presence (e.g., positive test result) or absence (e.g., negative test result) of the condition. The classifiers may be adjusted or tuned by adjusting parameters of the classifiers (e.g., a set of cutoff values used to classify a sample as described elsewhere herein, or weights of a neural network) to improve or optimize the performance metrics. The one or more classifiers may be adjusted or tuned so as to reduce an overall classification error (e.g., an “out-of-bag” or oob error rate for a Random Forest classifier). The one or more classifiers may be adjusted or tuned continuously during the training process (e.g., as sample datasets are added to the training set) or after the training process has completed.

[0246] The trained algorithm may comprise a plurality of classifiers (e.g., an ensemble) such that the plurality of classifications or outcome values of the plurality of classifiers may be combined to produce a single classification or outcome value for the sample. For example, a sum or a weighted sum of the plurality of classifications or outcome values of the plurality of classifiers may be calculated to produce a single classification or outcome value for the sample. As another example, a majority vote of the plurality of classifications or outcome values of the plurality of classifiers may be identified to produce a single classification or outcome value for the sample. In this manner, a single classification or outcome value may be produced for the sample having greater confidence or statistical significance than the individual classifications or outcome values produced by each of the plurality of classifiers.

[0247] After the trained algorithm is initially trained, a subset of the inputs may be identified as most influential or most important to be included for making high-quality classifications (e.g., having highest permutation feature importance). For example, a subset of the panel of condition-associated genomic loci may be identified as most influential or most important to be included for making high-quality classifications or identifications of conditions (or sub-types of conditions). The panel of condition- associated genomic loci, or a subset thereof, may be ranked based on classification metrics indicative of each influence or importance of each individual condition-associated genomic locus toward making high- quality classifications or identifications of conditions (or sub-types of conditions). Such metrics may beused to reduce, in some cases significantly, the number of input variables (e.g., predictor variables) that may be used to train the one or more classifiers of the trained algorithm to a desired performance level (e.g., based on a desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof).

[0248] For example, if training a classifier of the trained algorithm with a plurality comprising several dozen or hundreds of input variables to the classifier results in an accuracy of classification of more than 99%, then training the classifier of the trained algorithm instead with only a selected subset of no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100 such most influential or most important input variables among the plurality may yield decreased but still acceptable accuracy of classification (e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%).

[0249] As another example, if training a classifier of the trained algorithm with a plurality comprising several dozen or hundreds of input variables to the classifier results in a sensitivity or specificity of classification of more than 99%, then training the classifier of the trained algorithm instead with only a selected subset of no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100 such most influential or most important input variables among the plurality may yield decreased but still acceptable sensitivity or specificity of classification (e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%).

[0250] The subset of the plurality of input variables (e.g., the panel of condition-associated genomic loci) to the classifier of the trained algorithm may be selected by rank-ordering the entire plurality of input variables and selecting a predetermined number (e.g., no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100) of input variables with the best classification metrics (e.g., permutation feature importance).

[0251] Upon identifying the subject as having one or more conditions (e.g., a disease or disorder, such as first, second, and / or third disease condition), the subject may be optionally provided with a therapeuticintervention (e.g., prescribing an appropriate course of treatment to treat the one or more conditions of the subject). The therapeutic intervention may comprise a prescription of an effective dose of a drug, a further testing or evaluation of the condition, a further monitoring of the condition, or a combination thereof. If the subject is currently being treated for the condition with a course of treatment, the therapeutic intervention may comprise a subsequent different course of treatment (e.g., to increase treatment efficacy due to non-efficacy of the current course of treatment).

[0252] The therapeutic intervention may include prescribed medications or drugs, which may include one or more of: antimalarials, corticosteroids, immunosuppressants, and nonsteroidal anti-inflammatory drugs (NSAIDs). The therapeutic intervention may be effective to alleviate or decrease one or more symptoms, which may include one or more of: alopecia, anti-dsDNA seropositivity, arthritis, fever, hematuria, leukopenia, low serum complement, mucosal ulcer, myositis, pericarditis, pleurisy, proteinuria, pyuria, rash, thrombocytopenia, urinary cast, vasculitis, visual disturbance, or a combination thereof.

[0253] The therapeutic intervention may comprise recommending the subject for a secondary clinical test to confirm a diagnosis of the condition. This secondary clinical test may comprise an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[0254] The feature sets (e.g., comprising quantitative measures of a panel of condition-associated genomic loci) may be analyzed and assessed (e.g., using a trained algorithm comprising one or more classifiers) over a duration of time to monitor a patient (e.g., subject who has a condition or who is being treated for a condition). In such cases, the feature sets of the patient may change during the course of treatment. For example, the quantitative measures of the feature sets of a patient with decreasing risk of the condition due to an effective treatment may shift toward the profile or distribution of a healthy subject (e.g., a subject without the condition). Conversely, for example, the quantitative measures of the feature sets of a patient with increasing risk of the condition due to an ineffective treatment may shift toward the profile or distribution of a subject with higher risk of the condition or a more advanced stage or severity of the condition.

[0255] The condition of the subject may be monitored by monitoring a course of treatment for treating the condition of the subject. The monitoring may comprise assessing the condition of the subject at two or more time points. The assessing may be based at least on the feature sets (e.g., quantitative measures of a panel of condition-associated genomic loci) determined at each of the two or more time points. The therapeutic intervention may include prescribed medications or drugs, which may include one or more of: antimalarials, corticosteroids, immunosuppressants, and nonsteroidal anti-inflammatory drugs (NSAIDs). The therapeutic intervention may be effective to alleviate or decrease one or more symptoms, which may include one or more of: alopecia, anti-dsDNA seropositivity, arthritis, fever, hematuria, leukopenia, low serum complement, mucosal ulcer, myositis, pericarditis, pleurisy, proteinuria, pyuria, rash, thrombocytopenia, urinary cast, vasculitis, visual disturbance, or a combination thereof. The assessingmay be based at least on the presence, absence, or severity of one or more symptoms, such as alopecia, anti-dsDNA seropositivity, arthritis, fever, hematuria, leukopenia, low serum complement, mucosal ulcer, myositis, pericarditis, pleurisy, proteinuria, pyuria, rash, thrombocytopenia, urinary cast, vasculitis, visual disturbance, or a combination thereof.

[0256] In some embodiments, a difference in the feature sets (e.g., quantitative measures of a panel of condition-associated genomic loci) determined between the two or more time points may be indicative of one or more clinical indications, such as (i) a diagnosis of the condition of the subject, (ii) a prognosis of the condition of the subject, (iii) an increased risk of the condition of the subject, (iv) a decreased risk of the condition of the subject, (v) an efficacy of the course of treatment for treating the condition of the subject, and (vi) a non-efficacy of the course of treatment for treating the condition of the subject.

[0257] In some embodiments, a difference in the feature sets (e.g., quantitative measures of a panel of condition-associated genomic loci) determined between the two or more time points may be indicative of a diagnosis of the condition of the subject. For example, if the condition was not detected in the subject at an earlier time point but was detected in the subject at a later time point, then the difference is indicative of a diagnosis of the condition of the subject. A clinical action or decision may be made based on this indication of diagnosis of the condition of the subject, such as, for example, prescribing a new therapeutic intervention for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the diagnosis of the condition. This secondary clinical test may comprise an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[0258] In some embodiments, a difference in the feature sets (e.g., quantitative measures of a panel of condition-associated genomic loci) determined between the two or more time points may be indicative of a prognosis of the condition of the subject.

[0259] In some embodiments, a difference in the feature sets (e.g., quantitative measures of a panel of condition-associated genomic loci) determined between the two or more time points may be indicative of the subject having an increased risk of the condition. For example, if the condition was detected in the subject both at an earlier time point and at a later time point, and if the difference is a negative difference (e.g., the quantitative measures of a panel of condition-associated genomic loci increased from the earlier time point to the later time point), then the difference may be indicative of the subject having an increased risk of the condition. A clinical action or decision may be made based on this indication of the increased risk of the co...

Claims

CLAIMSWhat is claimed is:

1. A method for obtaining a gene set capable of classifying whether a patient has a first disease state, or a second disease state, the method comprising:(a) providing a dataset as an input to a machine learning classifier, wherein said dataset comprises or is derived from gene expression measurements from a plurality of reference samples of genes listed in a plurality of gene modules, wherein the plurality of reference samples comprises a first plurality of reference samples obtained or derived from reference subjects having the first disease state, and a second plurality of reference samples obtained or derived from reference subjects having the second disease state;(b) performing feature selection to select a subset of gene modules from the plurality of gene modules, wherein the plurality of gene modules forms the features of the machine learning classifier; and(c) selecting differentially expressed genes from the genes listed in the subset of gene modules to obtain the gene set capable of classifying whether a patient has the first disease state, or the second disease state.

2. The method of claim 1, wherein the machine learning classifier is sequential grouped feature importance (SGFI) algorithm.

3. The method of claim 1 or 2, wherein the feature selection comprises starting from a featureless model, and sequentially adding next best feature using leave-one-group-in importance (LOGI) until no further improvement in mean misclassification error (MMCE) over an improvement threshold is achieved.

4. The method of claim 3, wherein the improvement threshold is 0.00001, 0.00005, 0.0001, 0.0005, or 0.001.

5. The method of any one of claims 1 to 4, wherein the dataset is a batch corrected dataset.

6. The method of any one of claims 1 to 4, wherein the first disease state is having lupus and the second disease state is not having lupus.

7. The method of any one of claims 1 to 4, wherein the first disease state is having inactive lupus and the second disease state is not having lupus.

8. The method of any one of claims 1 to 4, wherein the first disease state is having active lupus and the second disease state is having inactive lupus.

9. The method of any one of claims 1 to 4, wherein the first disease state is having lupus nephritis and the second disease state is not having lupus.

10. The method of any one of claims 1 to 4, wherein the first disease state is having lupus nephritis and the second disease state is having non renal lupus.

11. The method of any one of claims 1 to 4, wherein the first disease state is having lupus and the second disease state is having rheumatoid arthritis.

12. The method of any one of claims 1 to 4, wherein the plurality of gene modules comprises gene modules listed in Table 1-1 to 1-46.

13. The method of any one of claims 1 to 12, further comprising analyzing a patient data set comprising or derived from gene expression measurement of at least 2 genes selected from the genes listed in the gene set obtained in step (c) to classify whether the patient has the first disease state or the second disease state, wherein the gene expression measurement is obtained from a biological sample obtained or derived from the patient.

14. The method of claim 13, wherein the patient data set comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in the gene set obtained in step (c).

15. The method of claim 13, wherein the patient data set comprises or is derived from gene expression measurements of the genes of the gene set obtained in step (c).

16. The method of any one of claims 13 to 15, wherein the method classifies whether the patient has the first disease state or the second disease state with an accuracy of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

17. The method of any one of claims 13 to 16, wherein the method classifies whether the patient has the first disease state or the second disease state with a sensitivity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

18. The method of any one of claims 13 to 17, wherein the method classifies whether the patient has the first disease state or the second disease state with specificity of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

19. The method of any one of claims 13 to 18, wherein the method classifies whether the patient has the first disease state or the second disease state with a positive predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

20. The method of any one of claims 13 to 19, wherein the method classifies whether the patient has the first disease state or the second disease state with a negative predictive value of at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

21. The method of any one of claims 13 to 20, wherein analyzing the patient data set comprises providing a patient data set as an input to a machine-learning model trained to generate an inference of indicative of whether the patient has the first disease state or the second disease state based on the patient data set.

22. The method of claim 21, further comprising: c) receiving, as an output of the machine-learning model, the inference; and d) electronically outputting a report classifying whether the patient has the first disease state or the second disease state based based on the inference.

23. The method of claim 21 or 22, wherein the machine-learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof.

24. The method of any one of claims 21 to 23, wherein the machine-learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) of at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more than about 0.99, for classifying a disease state of the patient.

25. The method of any one of claims 13 to 24, wherein the biological sample comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof.

26. The method of any one of claims 13 to 25, further comprising selecting, recommending and / or administering a treatment to the patient based on a classification whether the patient has the first disease state or the second disease state.

27. A method for classifying whether a patient has lupus, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or more Tables selected from Table 1-7, Table 1-28 and Table 1-40 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus; receiving, as an output of the machine learning model the inference; andelectronically outputting a report classifying whether the patient has lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

28. The method of claim 27, wherein the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or more Tables selected from Table 1-7, Table 1-28 and Table 1-40.

29. The method of claim 27 or 28, wherein Table 1-7 is selected.

30. The method of any one of claims 27 to 29, wherein Table 1-7 and Table 1-28 are selected.

31. The method of any one of claims 27 to 30, wherein Table 1-7, Table 1-28 and Table 1-40 are selected.

32. The method of claim 27, wherein the patient dataset comprising or derived from gene expression measurements of the genes listed in Table 1-7, Table 1-28 and Table 1-40.

33. A method for classifying whether a patient has inactive lupus or does not have lupus, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-1 and Table 1-7 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having inactive lupus or does not have lupus; receiving, as an output of the machine learning model the inference; and electronically outputting a report classifying whether the patient has inactive lupus or does not have lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

34. The method of claim 33, wherein the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123,124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-1, and Table 1-7.

35. The method of claim 33 or 34, wherein Table 1-7 is selected.

36. The method of any one of claims 33 to 35, wherein Table 1-1 and Table 1-7 are selected.

37. The method of claim 33, wherein the at least 2 genes selected comprises RSAD2, IFI6, HERC5, OAS3, IFI35, IFI27, IFI44, OASL, ISG15, IFI44L, IFIT1, IFIT3, OAS1, SAMD9L, HERC6, IFIT5, OAS2, IFITM1, EIF2AK2, SAMD9, GBP1, IFIT2, MX1, MX2, IFI30, GBP4, GBP2, CTLA4, SP110, ISG20, IFITM2, LAG3, SP100, CD160, KLRG1, HAVCR2, CD244, and ICOS.

38. A method for classifying whether a patient has active lupus or inactive lupus, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in Table 1-7, as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having active lupus or inactive lupus; receiving, as an output of the machine learning model the inference; and electronically outputting a report classifying whether the patient has active lupus or inactive lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

39. The method of claim 38, wherein the patient data set comprises gene expression measurements of the genes listed in Table 1-7.

40. A method for classifying whether a patient has lupus nephritis or does not have lupus, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7, and Table 1-28 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or does not have lupus; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or does not have lupus based on the inference,wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

41. The method of claim 40, wherein the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-28.

42. The method of claim 40 or 41, wherein Table 1-7 is selected.

43. The method of any one of claims 40 to 42, wherein Table 1-7 and Table 1-28 are selected.

44. The method of claim 40, wherein the at least 2 genes selected comprises IFI27, IFI44L, IFI44, IFITM1, OASL, RSAD2, SAMD9L, OAS3, IFI6, EIF2AK2, IFI35, HERC5, ISG15, GBP1, IFIT5, MX1, IFIT1, OAS2, LGALS3BP, SAMD9, HP, IFIT2, HERC6, MX2, IFIT3, MTF1, ASAP1, ISG20, GLS, SP110, IFITM2, IL1RN, GBP2, CD38, UBE2C, MARCKS, SPI1, OAS1, CASP1, B4GALT5, CDKN3, STAT4, BRCA1, SP100, TAP2, IFI30, SLAMF1, RELB, ARID3A, NFKBIA, FLNA, EGR1, NELL2, CXCL1, TNFAIP3, TYROBP, SOD2, ETAA1, MGLL, GP1BA, KMO, ACSL1, FCER2, ICAM1, TFF1, GMIP, PLAUR, INSIGI, G0S2, HHEX, IL1B, VEGFA, TNFAIP2, MAP3K4, SLC30A4, CTNND2, PIAS4, CCL20, GBP4, TAF15, NR3C1, EGR2, AK3, TBX3, CYP27B1, NFKB1, and CXCL345. A method for classifying whether a patient has lupus nephritis or non-renal lupus, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7 and Table 1-40 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus nephritis or non-renal lupus; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus nephritis or non-renal lupus based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

46. The method of claim 45, wherein the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-40.

47. The method of claim 45 or 46, wherein Table 1-7 is selected.

48. The method of any one of claims 45 to 47, wherein Table 1-7 and Table 1-40 are selected.

49. The method of claim 45, wherein the at least 2 genes selected comprises MRPL43, MRPL9, MRPL17, MRPL4, MRPL2, MRPL39, MRPL34, MRPL45, MRPL38, MRPL46, MRPL14, MRM1, MRPL30, MRPL13, MRPL3, HERC6, MRPL35, MRPL20, MRPL55, IFI35, MRPL42, MRPL18, MRPL50, OAS3, MRPL47, OAS1, IFI6, MRPL27, IFI44L, MRPL1, MRPL10, MRPL24, IFITM1, MRPL16, OAS2, MX1, MRPL33, IFI44, MX2, MRPL21, MRPL23, RSAD2, GBP4, MRPL44, IFIT3, ISG15, MRPL15, MRPL49, SAMD9L, IFIT1, IFI30, HERC5, SAMD9, MRPL51, EIF2AK2, IFIT5, MRPL37, OASL, SP100, MRPL32, MRPL11, MRPL48, MRPL53, MRPL19, MRPL41, MRPL52, MRPL28, IFI27, NSUN4, MRPL54, MRPL12, and IFIT2.

50. A method for classifying whether a patient has lupus or rheumatoid arthritis, the method comprising: providing a patient data set comprising or derived from gene expression measurements of at least 2 genes selected from genes listed in each of one or both Tables selected from Table 1-7, and Table 1-28 as an input to a machine learning model trained to generate an inference of whether the patient data set is indicative of the patient having lupus or rheumatoid arthritis; receiving, as an output of the machine learning model, the inference; and electronically outputting a report classifying whether the patient has lupus or rheumatoid arthritis based on the inference, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

51. The method of claim 50, wherein the patient dataset comprises or is derived from gene expression measurements ofat least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123,124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200 or all genes selected from the genes listed in each of the one or both Tables selected from Table 1-7 and Table 1-28.

52. The method of claim 50 or 51, wherein Table 1-7 is selected.

53. The method of any one of claims 50 to 52, wherein Table 1-7 and Table 1-28 are selected.

54. The method of claim 50, wherein the at least 2 genes selected comprises HERC6, OASL, ISG15, IFI27, IFITM1, IFI6, OAS2, HERC5, IFI35, IFIT3, OAS1, RSAD2, OAS3, EIF2AK2, IFI44L, ISG20, LGALS3BP, IL1RN, MX1, MX2, IFIT1, IFI44, SAMD9L, IFIT5, SP100, IFIT2, RPGR, SP110, RELB, MRPS15, NFKB1, TAP2, CYP27B1, PATJ, GBP1, ITGA6, BRCA1, NFKB2, TYROBP, FLNA, GBP4, SAMD9, CASP1, GMIP, AKAP10, SPI1, CXCL8, GLS, SH3BP5, KLF1, PTGS2, VEGFA, AK3, GBP2, UBE2C, SDC4, AMPD3, ASAP1, MARCKS, ACLY, DAB2, IFI30, CASP10, IL IB, ETAA1, EREG, BHMT, NR3C1, MGLL, CD38, CD37, TFF1, INSIG 1, TNFRSF11A, TSC22D1, PIAS4, TRAF1, CKB, HHEX, TBX3, EBI3, MAP3K4, MMP19, BHLHE41, INHBA, IFITM2, IL18, TNFAIP3, NFKBIA, HOMER2, APOL3, MSC, and EGR1.

55. The method of any one of claims 27 to 54, wherein the patient dataset is derived from the gene expression measurements using gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene co-expression network analysis (MEGENA), weighted gene co-expression network analysis (WGCNA), differential expression analysis, Z-score, log2 expression analysis, or any combination thereof.

56. The method of any one of claims 27 to 54, wherein the patient dataset is derived from the gene expression measurements using GSVA.

57. The method of claim 27 or 56, wherein a machine-learning model is trained using linear regression, logistic regression (LOG), Ridge regression, Lasso regression, elastic net (EN) regression, support vector machine (SVM), gradient boosted machine (GBM), k nearest neighbors (kNN), generalized linear model (GLM), naive Bayes (NB) classifier, neural network, Random Forest (RF), deep learning algorithm, linear discriminant analysis (LDA), decision tree learning (DTREE), adaptive boosting (ADB), Classification and Regression Tree (CART), hierarchical clustering, or any combination thereof.

58. The method of any one of claims 27 to 57, wherein the biological sample comprises a blood sample, isolated peripheral blood mononuclear cells (PBMCs), a tissue biopsy sample, or any derivative thereof.

59. The method of any one of claims 27 to 59, further comprising selecting, recommending and / or administering a treatment to a patient based on an inference.

60. The method of claim 59, wherein the treatment is any treatment undergoing testing for, or approved for, treatment of an inferred disease state.

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