Transcriptomic analysis identifies disease severity and therapeutic response for dermatological condition

Abstract

Provided herein are systems and methods for identifying a disease or disorder of a patient, identifying if a patient is likely to respond to a treatment for the disease or disorder, and / or predicting the clinical outcome of the disease or disorder of a patient. Systems and methods described herein may be directed to patients with different chronic conditions, inflammatory conditions, and / or autoimmune conditions. Systems and methods described herein may be directed to patients with a dermatological condition.

Description

[0001] TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISEASE SEVERITY AND

[0002] THERAPEUTIC RESPONSE FOR DERMATOLOGICAL CONDITION

[0003] CROSS-REFERENCE

[0004] [1] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 652,968, filed May 29, 2024, the contents of which are incorporated herein by reference in their entirety.

[0005] [2] The present disclosure relates generally to compositions, systems, devices, kits, and methods for disease prediction. Methods for disease prediction comprise methods for predicting the clinical outcome of a patient. Methods described herein comprise treating, preventing, or inhibiting a disease or disorder in a patient.

[0006] BACKGROUND

[0007] [3] Many systemic diseases, such as chronic, inflammatory, and / or autoimmune diseases, are heterogeneous in nature, and have variable causation, course and responsiveness to therapy. At least for these reasons, clinically relevant methods for determining a progression of a disease or disorder as well as the effectiveness of treatments are being explored. Understanding molecular mechanisms of disease variation and sorting patients based on underlying molecular mechanisms can be useful in developing targeted personalized therapy.

[0008] SUMMARY

[0009] [4] The present disclosure relates generally to compositions, systems, devices, kits, and methods for disease prediction, and uses thereof. In general, compositions, systems, devices, kits, and methods described herein may be useful in developing targeted personalized treatment for patients with chronic, inflammatory, and / or autoimmune diseases. In some embodiments, methods for treating, preventing, or inhibiting a disease or disorder in a patient comprise identifying subsets of patients or different disease phenotypes as related to gene expression data. In some embodiments, methods for treating, preventing, or inhibiting a disease or disorder in a patient comprise disease prediction of different subsets of patients as related to gene expression data. In some embodiments, methods for predicting the clinical outcome of a patient comprise identifying subsets of patients based on gene expression data. In some embodiments, methods described herein comprise complex data analysis for disease prediction. In some embodiments, disease prediction involves machine learning models, algorithms, and / or classifiers.

[0010] Certain Embodiments

[0011] [5] Provided herein are methods comprising: assaying an isolated biological sample from a patient to generate a data set comprising gene expression data, the assaying comprising: (a) performing an analysis with a microarray thereby measuring a concentration of a nucleic acid sequence from the biological sample or an amplicon thereof; (b) performing an RNA-Seq analysis to analyze the transcriptome of a biological sample by sequencing a complementary DNA (cDNA) synthesized from a nucleic acid sequence (RNA) from the biological sample or an amplicon thereof; or (c) quantitative polymerase chain reaction (qPCR) to measure the enrichment of a nucleic acid sequence from the biological sample or an amplicon thereof; and using a computer comprising a non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to run an application for identifying and comparing (i) the gene expression data generated from assaying the isolated biological sample to (ii) a reference gene expression data from one or more gene modules; electronically outputting a report detailing the comparison of (i) the gene expression data set generated from assaying the isolated biological sample to (ii) the reference gene expression data set from one or more gene modules; wherein the report: (i) identifies an immunological state of the patient at an accuracy of at least about 70%; (ii) identifies a disease or disorder or a susceptibility thereof of the patient at an accuracy of at least about 70%; (iii) identifies if the patient is likely to respond to a treatment comprising administration of a drug comprising an immunoregulator, an immunosuppressant, a steroid, an anti-inflammatory, a JAK inhibitors, a TNF inhibitors, an etanercept, an ustekinumab, a brodalumab, a dupilumab, a brepocitinib, a guselkumab, a secukinumab, a baricitinib, a corticosteroid, a nonsteroidal anti-inflammatory drug (NSAID), a tofacitinib, a upadacitinib, a deucravacitinib, a brepocitinib, a disease-modifying anti-rheumatic drug (DMARD), a conventional synthetic DMARD (csDMARD), a targeted synthetic DMARD (tsDMARD), a biologic DMARD (bDMARD), a biologic treatment, a TYK2 inhibitor, a TYK2 / JAK inhibitor, a combination inhibitor, a monoclonal antibody, an anti-TNF biologic, anti-IL-4 biologic, anti-IL-6 biologic, anti-IL-17 biologic, anti-IL- 12 / 23 biologic, anti- CD28 biologic, or combinations thereof; and / or (iv) identifies an effectiveness of the treatment of the patient as compared to the disease or disorder, or the progression of the disease or disorder; wherein: one or more gene modules comprise genes with similar gene expression data, similar gene expression profile, or coexpression; one or more gene modules comprise genes associated with a disease or disorder; each of the one or more gene modules comprise 2 or more genes as shown in TABLE 4; each of the one or more gene modules are associated with a different disease or disorder, treatment, process, or cell type as shown in FIG. 1-FIG. 20; or the disease or disorder is a chronic condition, an inflammatory condition, an autoimmune condition, a dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof; the isolated biological sample is a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a blood sample, a tissue sample, a purified cell sample, or combinations thereof; and optionally wherein the method comprising assaying an isolated biological sample from a patient further comprises purifying the isolated biological sample to obtain a purified cell sample.

[0012] [6] In some embodiments, a gene module of the one or more gene modules comprises 2 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more,

[0013] 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more,

[0014] 29 or more, 30 or more, 31 or more, 32 or more, 33 or more, 34 or more, 35 or more, 36 or more,

[0015] 37 or more, 38 or more, 39 or more, 40 or more, 41 or more, 42 or more, 43 or more, 44 or more,

[0016] 45 or more, 46 or more, 47 or more, 48 or more, 49 or more, or 50 or more genes associated with the gene module. In some embodiments, a gene module of the one or more gene modules comprises at least 2, 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, or 50 genes associated with a biological pathway. In some embodiments, the disease or disorder is the chronic condition. In some embodiments, the disease or disorder is the inflammatory condition. In some embodiments, the disease or disorder is the autoimmune condition. In some embodiments, the disease or disorder is the dermatological condition. In some embodiments, the disease or disorder is the skin disease. In some embodiments, the disease or disorder is the psoriasis. In some embodiments, the one or more gene modules are associated with a molecular endotype of a disease or disorder. In some embodiments, the one or more gene modules are associated with a phenotype. In some embodiments, the treatment comprises administration of a drug to the patient. In some embodiments, the treatment comprises parenteral administration of a drug to the patient. In some embodiments, the treatment comprises oral administration of a drug to the patient. In some embodiments, the treatment comprises administration for at least zero weeks, 16 weeks, and 52 weeks, at least 1 year, at least 2 years, at least 3 years, at least 4 years, at least 5 years, at least 6 years, at least 7 years, at least 8 years, at least 9 years, 10 years, at least 15 years, at least 20 years, at least 30 years, at least 35 years, at least 40 years, at least 45 years, at least 50 years, or at least the patient lifespan. In some embodiments, the treatment is adjusted as a function of the gene expression data. In some embodiments, the gene expression data is used to identify a drug for the treatment of the disease or disorder. In some embodiments, the report comprises nucleic acid sequencing data, transcriptome data, genome data, epigenetic data, proteome data, metabolome data, virome data, metabolome data, methylome data, lipidomic data, lineage-ome data, nucleosomal occupancy data, a genetic variant, a gene fusion, an indel, or combinations thereof. In some embodiments, the report comprises different formats. In some embodiments, the report comprises data from different sources, different studies, or combinations thereof. In some embodiments, the data is used to define a phenotype. In some embodiments, the phenotype is associated with a disease or disorder, a progression of a disease or disorder, a trajectory of a disease or disorder, a deterioration, a disability, an organ involvement, a medication response, a treatment response, a treatment target, a treatment, a treatment recommendation, a molecular endotype, or combinations thereof. In some embodiments, the phenotype is associated with a treatment adjusted as a function of a progression of a disease or disorder. In some embodiments, the progression of the disease or disorder comprises rate of deterioration of the patient over time. In some embodiments, the phenotype is associated with a treatment adjusted as a function of a trajectory of a disease or disorder. In some embodiments, the trajectory of the disease or disorder comprises a sequence of diagnoses over time. In some embodiments, the trajectory of the disease or disorder comprises patient decline comprising slow decline, gradual decline, stair step decline, or rapid decline. In some embodiments, the phenotype is associated with a treatment adjusted as a function of the progression of the disease or disorder. In some embodiments, the phenotype is associated with a treatment adjusted to comprise a different rate of administration, a different frequency of administration, administration of a different a drug, administration of more than one drug, administration of different combinations of drugs, or combinations thereof. In some embodiments, the one or more gene modules are a plurality of gene modules. In some embodiments, the method identifies more than one disease or disorder.

[0017] [7] Provided herein are kits for performing any of the methods described herein, the kit comprising a structural component as well as a composition component, wherein the composition component is a reaction mixture comprising an isolated biological sample from a patient and composition components and / or reagents for performing the assaying. In some embodiments, the structural component comprises a sample interface. In some embodiments, the structural component comprises a computer comprising a non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to run an application for identifying and comparing (i) the gene expression data generated from assaying the isolated biological sample to (ii) the reference gene expression data from one or more gene modules.

[0018] INCORPORATION BY REFERENCE

[0019] [8] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

[0020] BRIEF DESCRIPTION OF THE DRAWINGS

[0021] [9] The patent application file comprises 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.

[0022]

[0010] FIG. 1A-1C: Longitudinal changes in gene expression profiles of psoriatic skin treated with various drugs. FIG. 1A shows a heatmap visualizing the GSVA scores per sample for patients treated with different drugs at different timepoints (placebo, brepocitinib 100 mg, ustekinumab 90 mg, brodalumab 140 mg) as compared to different immune cell type and process modules (e.g., 48 gene signatures grouped and categorized into: cytokines, immune cell, immune or cell processes, metabolism, tissue cell), with each column representing a sample and each row corresponding to a different immune cell type and process module. Columns of patients were clustered using idealized k-means clustering and two different disease endotypes or clusters were identified (dark grey: increased inflammatory disease signature correlating to severe disease; light grey: decreased cytokine or immune cell signatures correlating to mild disease). As shown in the right vertical axis of FIG. 1A, Psoriasis Area and Severity Index (PASI) scores displayed at the top of the heat map as bar graphs visualize changes in gene expression profiles for patients in each of the four treatment groups (placebo, brepocitinib, ustekinumab, brodalumab). FIG. IB shows violin plots visualizing changes in gene expression profiles for patients in each of the four treatment groups (placebo, brepocitinib, ustekinumab, brodalumab) as compared to four gene signatures (neutrophil, keratinocytes, T Cell IL23, proteasome) signatures showing significant gene expression differences between timepoints of drug administration *P < 0.05; **P < 0.01; ***P < 0.001; ****p < 0.0001. FIG. 1AC shows alluvial plots visualizing changes in the k means cluster classification identifying two differentdisease endotypes or clusters (dark grey: increased inflammatory disease signature correlating to severe disease; light grey: decreased cytokine or immune cell signatures correlating to mild disease) of individual patients treated with different drugs at different timepoints.

[0023]

[0011] FIG 2A-2C: Changes in gene expression profiles of lesional psoriatic skin in response to various drugs. FIG 2A-2C shows GSVA scores for patients treated with different drugs at different timepoints as compared to different immune cell type and process modules (e.g., 48 gene signatures grouped and categorized into: cytokines, immune cell, immune or cell processes, metabolism, tissue cell), with each column representing a treatment group and each row corresponding to a different immune cell type and process module. Data visualizes changes in gene expression profile of lesional psoriatic skin to various therapeutic agents over time. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size) between baseline (week 0) and the designated time point. The asterisks indicate the p value of the change. Welch’s t test: *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001. The line graphs show the mean GSVA values over time for 7 gene signatures (neutrophil, keratinocytes, IFN, IL- 17 Complex, Th 17, T Cell IL23, TNF) that exhibited change over time. FIG. 2A shows changes in gene expression profiles for psoriatic skin in response to administration of placebo (PLC), brepocitinib 30 mg (BRE30), and brepocitinib 100 mg (BRE100)) after 1 weeks and 4 weeks. FIG. 2B shows changes in gene expression profiles for psoriatic skin in response to administration of placebo (PLC), brodalumab 140 mg (BROMO), and brodalumab 210 mg (BRO210)) after 4 weeks and 12 weeks. FIG. 2C shows changes in gene expression profiles for psoriatic skin in response to administration of guselkumab (GUS), etanercept 50 mg (ETN50), ustekinumab 45 mg (UST45), ustekinumab 90 mg (UST90) after 1 week and 12 weeks.

[0024]

[0012] FIG 3A-3C: Changes in gene expression profiles of nonlesional psoriatic skin in response to various drugs. FIG 3A-3C shows GSVA scores for patients treated with different drugs at different timepoints as compared to different immune cell type and process modules (e.g., 48 gene signatures grouped and categorized into: cytokines, immune cell, immune or cell processes, metabolism, tissue cell), with each column representing a treatment group and each row corresponding to a different immune cell type and process module. Data visualizes changes in gene expression profile of nonlesional psoriatic skin to various therapeutic agents over time. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size) between baseline (week 0) and the designated time point. The asterisks indicate the p value of the change. Welch’s t test: *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001. The line graphs show the mean GSVA values over time for 7 gene signatures (neutrophil, keratinocytes, IFN, IL- 17 Complex, Th 17, T Cell IL23, TNF) that exhibited change over time. FIG. 3A shows changes in gene expression profiles for psoriatic skin in response to administration of placebo (PLC), brepocitinib 30 mg (BRE30), and brepocitinib 100 mg (BRE100)) after 2 weeks and 4 weeks. FIG. 3B shows changes in gene expression profiles for psoriatic skin in response to administration of placebo (PLC), brodalumab 140 mg (BROMO), and brodalumab 210 mg (BRO210)) after 4 weeks and 12 weeks. FIG. 3C shows changes in gene expression profiles for psoriatic skin in response to administration of guselkumab (GUS), etanercept 50 mg (ETN50), ustekinumab 45 mg (UST45), ustekinumab 90 mg (UST90) after 1 week and 12 weeks.

[0025]

[0013] FIG. 4A-4D: Development of the Psoriasis Cell and Immune Score (PsoriaCIS). FIG. 4A shows a heatmap visualizing the GSVA scores of baseline lesional (LES) and non- lesional (NLS) samples of psoriasis patients from gene expression data used in the development of PsoriaCIS as compared to different immune cell type and process modules (e.g., 48 gene signatures grouped and categorized into: cytokines, immune cell, immune or cell processes, metabolism, tissue cell), with each column representing a sample and each row corresponding to a different immune cell type and process module. Columns of patients were clustered using idealized k-means clustering and different patient cohorts were identified. FIG. 4B shows results of logistic regression with ridge penalty carried out between lesional (LES) and non-lesional (NLS) samples at baseline and its coefficients are visualized as bar graphs. FIG. 4C shows violin plots visualizing PASI and PsoriaCIS scores of patients in four different patient cohorts identified by k-means clustering, displaying the pattern of change. FIG. 4D shows Scatter plot showing pearson correlation between PASI and PsoriaCIS for patients in FIG. 4A.

[0026]

[0014] FIG. 5A-5B: Comparison of Psoriasis Cell and Immune Score (PsoriaCIS) with Psoriasis Area and Severity Index (PASI). FIG. 5A shows box plots of PASI (top) and PsoriaCIS scores (bottom) across various timepoints for Groups A, B and C psoriasis patients. Repeated measures of ANOVA were used to calculate significant differences between timepoints. FIG. 5B shows scatter plots showing Pearson correlations between PASI and PsoriaCIS across multiple timepoints. *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001, ns is not significant.

[0027]

[0015] FIG. 6A-6B: Baseline molecular differences in lesional skin compared to nonlesional skin among clinical responders and nonresponders. FIG. 6A shows GSVA using 48 informative gene modules was used to evaluate baseline molecular differences in psoriatic lesional skin compared to nonlesional skin separately for responders (left) and nonresponders (right) to brodalumab 140 or 210 mg, brepocitinib 30 mg, etanercept 50 mg and ustekinumab 90 mg. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size) between baseline lesional and nonlesional samples. FIG. 6B shows violin plots of 9 gene modules displaying significant differences between cumulative GSVA scores of lesional and nonlesional samples separately for responders and nonresponders. The asterisks in heatmap and violin plots indicate the p value of the change in each module. Welch’s t test: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

[0028]

[0016] FIG. 7A-7B: Molecular differences in psoriatic lesional skin over time treated with various therapeutic agents. FIG. 7A shows heatmap of GSVA scores of placebo, brodalumab 210 mg, brepocitinib 30 mg, guselkumab, etanercept 50 mg, and ustekinumab 45 mg treated patients. FIG. 7B shows alluvial plots visualizing changes in the k means cluster classification identifying two differentdisease endotypes or clusters (dark grey: increased inflammatory disease signature correlating to severe disease; light grey: decreased cytokine or immune cell signatures correlating to mild disease) of individual patients treated with different drugs at different timepoints shows.

[0029]

[0017] FIG. 8: Secukinumab validation dataset analysis: shows heatmap showing GSVA scores at five timepoints (Baseline, Day 4, Day 14, Day 42 and Day 82) of lesional skin of 13 Psoriasis patients treated with 300 mg of Secukinumab.

[0030]

[0018] FIG. 9A-9G: Line graphs displaying the changes in mean GSVA scores over time: shows line graphs of 48 gene signatures are visualized in 5 groups (immune cell, cytokines, metabolism, tissue cells, and immune cell processes). Each line graph has baseline NL, LS and lesional timepoint (either Week 1 or Week 2 or Week 4, or Week 12) based on Group A B or C as defined in FIG S5.

[0031]

[0019] FIG. 10: Elbow and silhouette plots showing optimal number of clusters for K means: The elbow and silhouette plots for each therapeutic agent are organized as heatmaps of FIG 1 and SI. Elbow plot (top) displaying within-cluster sum of squares (WSS) on Y-axis and number of clusters K on X-axis, silhouette plot (bottom) displaying silhouette score on Y-axis and number of clusters on X axis. The optimal number of clusters (K=2) aligns with the highest silhouette score for each therapeutic agent.

[0032]

[0020] FIG. 11: Schematic overview and grouping of psoriasis datasets used in this study. Group A contains baseline LS and NL, Week 2 LS and Week 4 LS skin biopsies of psoriasis patients treated with either placebo or brepocitinib (30 mg or 100 mg). Group B contains baseline LS and NL, Week 4 LS, Week 12 LS and NL skin biopsies of psoriasis patients treated with either placebo, brodalumab (140 mg or 210 mg). Group C contains baseline LS and NL, Week 1 LS, Week 12 LS skin biopsies of psoriasis patients treated with either guselkumab, etanercept (50 mg) or ustekinumab (45 mg or 90 mg).

[0021] FIG. 12: Line graphs displaying the changes in mean GSVA scores over time: Line graphs of 48 gene signatures are visualized in 5 groups (immune cell, cytokines, metabolism, tissue cells, and immune cell processes). Each line graph has baseline NL, LS and lesional timepoint (either Week 1 or Week 2 or Week 4, or Week 12) based on Group A B or C.

[0033]

[0022] FIG. 13A-13B: Changes in molecular features of psoriatic skin at Week 12 in response to Group B agents: GSVA with 48 informative gene modules was employed to evaluate the impact of treatment on nonlesional psoriatic skin. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size). FIG. 13A between week 12 nonlesional and baseline nonlesional and FIG. 13B between week 12 lesional and week 12 nonlesional. The heatmap shows increase in effect size as compared to decrease in effect. The asterisks indicate the p value of the change in each module. Welch’s t test: *P < 0.05; **P <

[0034] 0 01; ***p < 0.001; ****P < 0.0001.

[0035]

[0023] FIG. 14A-14C: Changes in gene expression profiles of psoriatic skin in response to various therapeutic agents among responders and nonresponders: GSVA using 48 informative gene modules was used to evaluate the molecular response of lesional psoriatic skin to various therapeutic agents over time. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size) between baseline lesional (week 0) and the designated time point separately for responders and nonresponders. The heatmap shows increase in effect size as compared to decrease in effect. The asterisks indicate the p value of the change in each module. Welch’s t test: *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001. FIG. 14A indicates brepocitinib (30 mg) at weeks 2 and 4. FIG. 14B indicates placebo and brodalumab (140 and 210 mg) at weeks 4 and 12. FIG. 14C represents data for etanercept (50mg), ustekinumab (90 mg) after 1 and 12 weeks.

[0036]

[0024] FIG. 15A-15C: Changes in psoriatic lesional skin compared to baseline nonlesional skin in response to various therapeutic agents among responders and nonresponders.

[0037] GSVA using 48 informative gene modules was used to evaluate whether various therapeutic agents restored psoriatic lesional skin to that expressed by nonlesional skin over time. Heatmaps show the magnitude of change in GSVA score of each gene module (Hedges G effect size) between baseline nonlesional (week 0) and the designated time point separately for responders and nonresponders. The heatmap shows increase in effect size as compared to decrease in effect. The asterisks indicate the p value of the change in each module. Welch’s t test: *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001. FIG. 15A indicates brepocitinib (30 mg) at week 0, weeks 2 and 4. FIG. 15B indicates placebo and brodalumab (140 and 210 mg) at week 0, weeks 4 and 12. FIG. 15C represents data for etanercept (50mg), ustekinumab (90 mg) at week 0 and after 1 and 12 weeks.

[0038]

[0025] FIG. 16A-16B: Comparison of PASI and PsoriaCIS in responders and nonresponders. FIG. 16A Dot plots of PASI (top) and PsoriaCIS (bottom) across various timepoints for Groups A, B and C psoriasis patients. Repeated measures of ANOVA or paired t tests were used to calculate significant differences between timepoints. FIG. 16B Scatter plots showing pearson correlation between PASI and PsoriaCIS across multiple timepoints. *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001, ns is not significant.

[0039]

[0026] FIG. 17A-17C: Molecular Heterogeneity in psoriatic lesional skin at baseline: FIG. 17A shows a heatmap visualizing the GSVA scores calculated using 48 informative modules based on baseline lesional skin samples only with each column representing a sample and each row corresponding to a different module. Columns of patients were clustered using idealized k- means clustering and four different disease endotypes or clusters were identified (red, green, blue, yellow). As shown in the right vertical axis of FIG. 17A, PASI and PsoriaCIS scores are displayed at the top of the heat map as bar graphs visualize changes in gene expression profiles for patients. Responders and nonresponders are labelled accordingly. FIG. 17B Violin plots of PASI and PsoriaCIS scores of the patients in four K means clusters displaying the pattern of change. FIG. 17C Table showing statistical differences (p values) between PASI and PsoriaCIS scores across different clusters in responders and nonresponders.

[0040]

[0027] FIG. 18A-18C: Changes in gene expression profiles for patients with atopic dermatitis from skin samples 16 weeks after Dupilumab treatment. FIG. 18A shows a heatmap visualizing the change in GSVA scores as related to or each gene module in different categories (cytokines, immune cell, immune_or_cell_processes, metabolism, skin barrier, T- cell subset, tissue cell) for patients with atopic dermatitis 16 weeks after treatment (7 patients treated with placebo), as compared to patients with atopic dermatitis before treatment, and healthy patients. FIG. 18B shows a heatmap visualizing the change in GSVA scores as related to or each gene module in different categories (cytokines, immune cell, immune_or_cell_processes, metabolism, skin barrier, T-cell_subset, tissue cell) for patients with atopic dermatitis 16 weeks after treatment (13 patients treated with dupilumab (DUP)), as compared to patients with atopic dermatitis before treatment, and healthy patients. As shown in the right vertical axis of FIG. 18A and FIG. 18B, EASI and SCORAD scores are displayed at the top of the heat map as bar graphs visualize changes in gene expression profiles for patients. FIG. 18C shows the change in GVSA scores as statistical differences (p values; Welch’s t test: *P < 0.05; **P < 0.01; ***p < 0.001; ****p < 0.0001) for patients treated with placebo and patients treated with dupilumab.

[0041]

[0028] FIG. 19A-19B: Changes in gene expression profiles for patients with atopic dermatitis from blood samples 3 months after Dupilumab treatment. FIG. 19A shows a heatmap visualizing the change in GSVA scores as related to or each gene module in different categories (right vertical axis) for patients with atopic dermatitis 3 months after dupilumab treatment (each column is a patient sample), as compared to patients with atopic dermatitis before dupilumab treatment. FIG. 19B Violin plots of GVSA scores of patients (before and after dupilumab treatment) in four K means clusters displaying the pattern of change.

[0042]

[0029] FIG. 20A-20B: Endotypes related to gene expression profiles for patients with atopic dermatitis from blood samples 3 months after Dupilumab treatment. FIG. 20A shows a heatmap visualizing the change in GSVA scores as related to or each gene module in different categories (right vertical axis) for patients with atopic dermatitis 3 months after dupilumab treatment (each column is a patient sample), as compared to patients with atopic dermatitis before dupilumab treatment, resulting in the identification of 3 endotypes (cluster 1 or eType- 1, cluster 2 or eType-2, cluster 3 or eType-3) by k-means clustering. FIG. 20B shows the change in GVSA scores as statistical differences (p values; Welch’s t test: *P < 0.05; **P < 0.01; ***P < 0.001; ****p < 0.0001) for patients associated with each of the 3 endotypes.

[0043] DETAILED DESCRIPTION

[0044]

[0030] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and explanatory only, and are not restrictive of the disclosure.

[0045]

[0031] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0046]

[0032] All documents, or portions of documents, cited in this application, including, but not limited to, patents, patent applications, articles, books, and treatises, are hereby expressly incorporated by reference in their entirety for any purpose.

[0047] Definitions

[0048]

[0033] Unless otherwise indicated, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Unless otherwise indicated or obvious from context, the following terms have the following meanings:

[0049]

[0034] The terms, “a,” “an,” and “the,” as used herein, comprise plural references unless the context clearly dictates otherwise.

[0035] The terms, “or” and “and / or,” as used herein, comprise any and all combinations of one or more of the associated listed items.

[0050]

[0036] The terms, “including,” “comprises,” “comprised,” and other forms, are not limiting.

[0051]

[0037] The terms, “comprise” and its grammatical equivalents, as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052]

[0038] The term, “about,” as used herein in reference to a number or range of numbers, is understood to mean the stated number and numbers + / - 10% thereof, or 10% below the lower listed limit and 10% above the higher listed limit for the values listed for a range.

[0053]

[0039] The term, “disease,” as used herein, refers to comprising pathway conditions or pathway systems that are not conducive to cell survival, tissue survival, systemic survival, or organism survival. In some instances, the term “disease” may be used interchangeably with the term “disease state” or “disease or disorder”.

[0054]

[0040] The term, “nucleic acid,” as used herein, refers to a polymer of nucleotides. A nucleic acid may comprise ribonucleotides, deoxyribonucleotides, combinations thereof. A nucleic acid may be single-stranded or double-stranded, unless specified. Non-limiting examples of nucleic acids are double stranded DNA (dsDNA), single stranded (ssDNA), messenger RNA, genomic DNA, and cDNA. Accordingly, nucleic acids as described herein may comprise one or more mutations, one or more irregularities, or both.

[0055]

[0041] The term “patient” as used herein, refers to an animal. The term “patient” may be used interchangeably with the term “subject”, “test subject”, “reference subject”, “patient”, “test patient” or “reference patient”. In some instances, the patient or subject is a mammal. In some instances, the patient or subject is a human. In some instances, the patient or subject is diagnosed or at risk for a disease. In some instances, the patient or subject is diagnosed or at risk of more than one disease. In some instances, the patient or subject has not received treatment for the disease. In some instances, the patient or subject has received treatment for the disease.

[0056]

[0042] The term, “sample,” as used herein, refers to something comprising a gene of interest. In some instances, the sample is a biological sample, such as a biological fluid or tissue sample. In some instances, the sample is a biological sample isolated from a patient or subject. In some instances, the sample is a biological sample or environmental sample that is modified or manipulated. By way of non-limiting example, samples may be modified or manipulated with purification techniques, digestion techniques, heat, nucleic acid amplification, salts and buffers. In some instances, purification of a sample results in a purified cell sample.

[0057]

[0043] 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 collection comprising one or more members.

[0058]

[0044] The terms, “treatment” and “treating,” as used herein, refer to a pharmaceutical or other intervention regimen for obtaining beneficial or desired results in the patient or subject receiving the treatment. Beneficial or desired results comprise but are not limited to a therapeutic benefit and / or a prophylactic benefit. A therapeutic benefit may refer to eradication or amelioration of symptoms or of an underlying disorder being treated. Also, a therapeutic benefit can be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disorder such that an improvement is observed in the patient, notwithstanding that the patient may still be afflicted with the underlying disorder. A prophylactic effect comprises delaying, preventing, or eliminating the appearance of a disease or condition, delaying, or eliminating the onset of symptoms of a disease or condition, slowing, halting, or reversing a progression of a disease or disorder, or any combination thereof. For prophylactic benefit, a patient at risk of developing a particular disease, or to a patient reporting one or more of the physiological symptoms of a disease may undergo treatment, even though a diagnosis of this disease may not have been made. By way of non-limiting example, a treatment comprises a therapy. In some instances, the treatment comprises personalized therapy. In some instances, the treatment results in therapeutic benefits that are conducive to cell survival, tissue survival, systemic survival, or organism survival.

[0059]

[0045] Various terms used throughout the present description may be read and understood as follows, unless the context indicates otherwise: “or” as used throughout is inclusive, as though written “and / or”; singular articles and pronouns as used throughout comprise their plural forms, and vice versa; similarly, gendered pronouns comprise their counterpart pronouns so that pronouns should not be understood as limiting anything described herein to use, implementation, performance, etc. by a single gender; “exemplary” should be understood as “illustrative” or “exemplifying” and not necessarily as “preferred” over other embodiments. Further definitions for terms may be set out herein; these may apply to prior and subsequent instances of those terms, as will be understood from a reading of the present description.

[0060]

[0046] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0061]

[0047] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0062]

[0048] 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.

[0063]

[0049] The terms “embodiments,” “some 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 comprised in at least some embodiments, but not necessarily all embodiments, of the present disclosure.

[0064] Introduction

[0065]

[0050] Some aspects of the present disclosure are directed to methods and systems for determining a gene set or gene module capable of classifying a disease or disorder of a patient. In some embodiments, a gene module comprises genes grouped in consideration of gene expression data associated with a disease or disorder. In some embodiments, one or more gene modules comprise genes grouped in consideration of gene expression data associated with a disease or disorder. In some embodiments, one or more gene modules comprise a plurality of gene modules associated with a disease or disorder. The gene modules can be used to classify, and / or treat a disease or disorder of a patient. In some embodiments, classifying a disease or disorder of a patient comprises determining whether that patient has the disease and / or which endotype out of two or more endotypes of the disease the patient has. In some embodiments, methods described herein comprise identifying and / or providing targeted therapy for a patient based on the disease or disorder classification of the patient. In some embodiments, the methods described herein comprise the grouping of genes in consideration of gene expression data and analyzing the gene expression data of the group of genes to determine a gene module. In some embodiments, the methods described herein comprise the grouping of more than one gene module in consideration of the gene expression data of the genes in the gene module. In some embodiments, the methods described herein comprise the use of machine learning methods, machine learning models, data analysis, data categorization, or combinations thereof.

[0066] Assays

[0067]

[0051] Provided herein are assays for the generation of gene expression data from a sample from a patient. In some embodiments, the gene expression data from the sample from the patient is indicative of a disease or disorder. In some embodiments, the gene expression data from the sample from the patient is indicative of the progression of a disease or disorder. In some embodiments, the sample is a biological sample isolated from the patient. In some embodiments, the sample is a whole blood sample, a peripheral blood sample, a peripheral blood mononuclear cell sample, a blood sample, a tissue sample, a purified cell sample, or combinations thereof. In some embodiments, the assays for the generation of gene expression data from a sample from a patient are gene expression assays.

[0068]

[0052] Also provided herein are methods and systems described herein comprising assays for the generation of gene expression data from the sample from the patient, for accurate, repeatable, real-time, determinations of a disease or disorder in a patient (e.g., classifying a disease or disorder, determining progression of disease or disorder, predicting a clinical outcome of a disease or disorder), useful in developing targeted personalized treatment for patients with chronic, inflammatory, and / or autoimmune diseases as described herein.

[0069] Gene expression Assays

[0070]

[0053] In some embodiments, systems, compositions, methods, kits, and solutions described herein comprise assaying a biological sample from a patient. In some embodiments, systems, compositions, methods, kits, and solutions described herein comprise generating gene expression data. In some embodiments, methods and systems described herein comprise assaying a biological sample from a patient. In some embodiments, methods and systems described herein comprise assaying a biological sample from a patient to generate gene expression data. In some embodiments, methods and systems described herein comprise assaying an isolated biological sample from a patient to generate a dataset comprising gene expression data. In some embodiments, the methods and systems described herein comprise the use of assays to generate gene expression data. In some embodiments, the methods and systems described herein comprise the use of assays to generate gene expression data from a biological sample from a patient. In some embodiments, the methods and systems described herein comprise the use of assays to generate gene expression data that is indicative of a disease or disorder. In some embodiments, the methods and systems described herein comprise the use of microarray assays, RNA-Seq assays, quantitative polymerase chain reaction (qPCR) assays, or combinations thereof.

[0071] Microarray Assay

[0072]

[0054] In some embodiments, methods and systems described herein comprise the use of microarray assays to generate gene expression data from a biological sample from a patient. In some embodiments, methods and systems described herein comprise the use of microarray assays to generate gene expression data that is indicative of a disease or disorder. In some embodiments, methods and systems described herein comprise the use of microarray assays to generate gene expression data from a biological sample from a patient to identify a disease or disorder in a patient.

[0073]

[0055] In some embodiments, a microarray assay comprises measuring the concentration of a nucleic acid sequence in a biological sample. In some embodiments, a microarray assay comprises binding nucleic acids to a surface to measure the concentration of a nucleic acid sequence in a biological sample. In some embodiments, a microarray assay comprises binding 1000’s of nucleic acids to a surface to measure the concentration of a nucleic acid sequence in a biological sample, or an amplicon thereof. In some embodiments, the microarray assay comprises binding 1000’s of nucleic acids to a surface to measure the relative concentration of nucleic acid sequences in a mixture via hybridization and subsequent detection of the hybridization events. In some embodiments, the microarray assay comprises a nucleic acid array. In some embodiments, the microarray assay comprises a DNA microarray. In some embodiments, the microarray assay measures the concentration of a nucleic acid sequence of interest in a sample. In some embodiments, the concentration of the nucleic acid sequence of interest is gene expression data.

[0074]

[0056] In some embodiments, the microarray assay comprises the deposit or synthesis of a nucleic acid sequence (e.g., DNA sequence). In some embodiments, thea microarray assay comprises the deposit of a nucleic acid sequence on a surface or synthesis of a nucleic acid sequence on a surface.

[0075]

[0057] In some embodiments, the microarray assay comprises a solution comprising labeled nucleic acids and probes attached to a surface. In some embodiments, the microarray assay comprises the solution comprising labeled nucleic acids and probes attached to a surface, such that the binding of the labeled nucleic acids to the probes attached to the surface results in measuring the concentration of a nucleic acid sequence of interest. In some embodiments, the sample from the patient is processed such that the solution comprising labeled nucleic acids comprises nucleic acids from the patient sample from patient. In general, the microarray assay probes are used to measure the concentration of a nucleic acid sequence in a solution or sample. In some embodiments, the microarray assay comprises different probes used to measure the concentration of different types of nucleic acid sequences in a solution or sample. In some embodiments, the microarray assay probes are used to measure the concentration of different nucleic acid sequences in a solution or sample. In some embodiments, the nucleic acid as described herein is a target nucleic acid. In some embodiments, the nucleic acid from a biological sample as described herein is a target nucleic acid. In some embodiments, the nucleic acid sequence as described herein is a target nucleic acid sequence. In some embodiments, the nucleic acid sequence from a biological sample as described herein is a target nucleic acid sequence.

[0076]

[0058] Provided herein are microarrays or DNA microarrays for measuring gene expression levels. In some embodiments, microarrays or DNA microarrays generate gene expression data. In some embodiments, the microarray assay comprises extracting a nucleic acid from cells in the biological sample from the patient.

[0077]

[0059] In some embodiments, the microarray assay comprises isolating a nucleic acid from cells in the biological sample from the patient. In some embodiments, the microarray assay comprises isolating the nucleic acid from cells, enriching the nucleic acid, and labeling the nucleic acid with fluorescent labels or detectable tags. In some embodiments, the labeled nucleic acid is detectable by measuring detectable signals or fluorescent signals. In some emboidments, the labeled nucleic acid hybridizes to probes attached to the surface as described herein. In some embodiments, the surface as described herein is the surface of the array. In some embodiments, the array is washed to remove sample components that are not bound or did not hybridize. In some embodiments, the sample is labeled and the array is stained with a different label to generate a detectable signal. In some embodiments, detectable signal or fluorescent signal from the labeled nuclei acid is measured by scanning the surface. In some embodiments, the intensity of the signal from the labeled nucleic acid is indicative of the concentration of the labeled nucleic acid. In some embodiments, the intensity of the signal in different areas of the surface, corresponding to different probes bound to different labeled nucleic acids, is a measure of gene expression. In some embodiments, different areas of the surface correspond to a different gene.

[0078]

[0060] In some embodiments, the microarray assay comprises isolating RNA from cells in the biological sample from the patient. In some embodiments, the microarray assay comprises isolating RNA from cells, enriching for mRNA, and optionally amplifying RNA. In some embodiments, the microarray assay comprises isolating RNA from cells and (i) labeling the RNA directly; (ii) converting the RNA to a labeled cDNA; or (iii) converting the RNA to an RNA promoter tailed cDNA which is further converted to cRNA. In some embodiments, the cRNA is labeled cRNA. In some embodiments, labeling the RNA comprises the incorporation of nucleotide labels such as fluorescently labeled nucleotides during synthesis. In some embodiments, labeling the RNA comprises the incorporation of fluorescently labeled nucleotides. In some embodiments, labeling the RNA comprises the incorporation of detectable tags for nucleotide detection.

[0079]

[0061] In some embodiments, the nucleic acid as described herein is a target nucleic acid. In some embodiments, the nucleic acid from a biological sample as described herein is a target nucleic acid. In some embodiments, the nucleic acid sequence as described herein is a target nucleic acid sequence. In some embodiments, the nucleic acid sequence from a biological sample as described herein is a target nucleic acid sequence.

[0080] RNA Sequencing (RNA-Seq)

[0081]

[0062] Provided herein are methods for assaying a biological sample from a patient comprising sequencing a nucleic acid from a patient. In some embodiments, the nucleic acid is an RNA. In some embodiments, the methods for assaying are methods for sequencing RNA or methods of RNA sequencing (RNA-Seq).

[0082]

[0063] In some embodiments, methods for sequencing RNA comprise converting RNA to DNA. In some embodiments, methods for sequencing RNA comprise the use of reverse transcriptase (RT) to synthesize complementary DNA (cDNA) from RNA. In some embodiments, methods for sequencing RNA comprise next generation sequencing (NGS) directed to RNA. In some embodiments, methods for sequencing RNA are transcriptomic techniques. In some embodiments, NGS directed to RNA profiles the transcriptome, measuring the activity of thousands of genes at once using high-throughput techniques. In some embodiments, NGS comprises High-Throughput Sequencing (HTS).

[0083]

[0064] In some embodiments, RNA-Seq is a method of transcriptome profiling comprising NGS. In some embodiments, RNA-Seq comprising high-throughput techniques detects nucleic acids present in low concentrations, and / or nucleic acids comprising polymorphisms, mutations, isoforms, or other variations. In some embodiments, RNA-Seq measures non-coding RNAs such as micro-RNAs. In some embodiments, RNA-Seq comprises the detection of different RNA species, including mRNA, non-coding RNA, pathogen RNA, chimeric gene fusions, transcript isoforms, splice variants, and previously unidentified transcripts. In some embodiments, RNA- Seq comprises a comprehensive view of transcript abundance. In some embodiments, RNA-Seq comprises the detection of rare RNA transcript variants, and supports the detection of mutations and germline variation for thousands of expressed genetic variants. In some embodiments, RNA- Seq comprises the assessment of dynamic changes in gene expression in response to various stimuli. In some embodiments, RNA-Seq comprises the identification of biomarkers and / or molecular signatures associated with a disease or disorder, or a subtype of a disease or disorder. In some embodiments, RNA-Seq comprises the identification of molecular signatures associated with responses to a treatment of a disease or disorder (e.g., administration of a drug, treatment of a disease or disorder). In some embodiments, RNA-Seq comprises the identification of a molecular endotype.

[0084]

[0065] In some embodiments, RNA-Seq is a rapid, precise, quantitative measurement of gene expression to generate gene expression data from a cell in an isolated biological sample from a patient. In some embodiments, RNA-Seq measures the activity of a complete set of a patient’s transcribed genes.

[0085]

[0066] In some embodiments, methods for sequencing RNA comprise isolating a nucleic acid from a cell in a biological sample from a patient as described herein. In some embodiments, methods for sequencing RNA comprise isolating the nucleic acid from a cell, enriching the nucleic acid, and optionally amplifying the nucleic acid as described herein. In some embodiments, the methods for sequencing RNA comprise labeling the nucleic acid with fluorescent labels or detectable tags. In some embodiments, the labeled nucleic acid is detectable by measuring detectable signals or fluorescent signals. In some emboidments, a labeled nucleic acid generate a detectable signal. In some embodiments, detectable signal or fluorescent signal from the labeled nucleic acid is measured or quantified as light intensity. In some embodiments, the intensity of the signal from the labeled nucleic acid is indicative of the concentration of the labeled nucleic acid. In some embodiments, the intensity of the signal is a measure of gene expression.

[0086]

[0067] In some embodiments, methods for sequencing RNA comprise isolating RNA from a cell in an isolated biological sample as described herein. In some embodiments, methods for sequencing RNA comprise isolating RNA from a cell, enriching for mRNA, and optionally amplifying RNA. In some embodiments, methods for sequencing RNA comprise isolating RNA from a cell and (i) labeling the RNA directly; (ii) converting the RNA to a labeled cDNA; or (iii) converting the RNA to an RNA promoter tailed cDNA which is further converted to cRNA. In some embodiments, the cRNA is labeled cRNA. In some embodiments, labeling the RNA comprises the incorporation of nucleotide labels such as fluorescently labeled nucleotides during synthesis. In some embodiments, labeling the RNA comprises the incorporation of fluorescently labeled nucleotides. In some embodiments, labeling the RNA comprises the incorporation of detectable tags for nucleotide detection.

[0087]

[0068] In some embodiments, methods for sequencing RNA comprise isolating RNA from an isolated biological sample as described herein. In some embodiments, the isolated biological sample is a blood sample. In some embodiments, the blood sample comprises blood cells. In some embodiments, methods for sequencing RNA comprise isolating RNA from blood cells, enriching for mRNA, and optionally amplifying RNA as described herein. In some embodiments, methods for sequencing RNA comprise isolating RNA from blood cells and (i) labeling the RNA directly; (ii) converting the RNA to a labeled cDNA; or (iii) converting the RNA to an RNA promoter tailed cDNA which is further converted to cRNA. In some embodiments, the cRNA is labeled cRNA. In some embodiments, labeling the RNA comprises the incorporation of nucleotide labels such as fluorescently labeled nucleotides during synthesis. In some embodiments, labeling the RNA comprises the incorporation of fluorescently labeled nucleotides. In some embodiments, labeling the RNA comprises the incorporation of detectable tags for nucleotide detection.

[0088]

[0069] In some embodiments, RNA-Seq comprises: (i) isolating RNA from a cell, (ii) enriching for mRNA, (iii) contacting the mRNA with a reverse transcriptase (RT) which synthesize cDNA using mRNA as a template; (iv) fragmenting mRNA or cDNA to short sequences (about 200-500 base pairs of length); (v) amplifying the short sequences; (vi) sequencing the short sequences to generate reads; (vii) aligning the reads to a reference genome; (viii) determining gene expression as the quantity of reads that map to gene exons, absolute read counts, and / or nomalized values such as RPKM (reads per kilobase per million mapped reads). In some embodiments, the sequences of the reads are gene transcripts. In some embodiments, reverse transcriptases (RT) is an enzyme used in RNA sequencing (RNA-Seq) to synthesize a complementary DNA (cDNA) from RNA.

[0089]

[0070] In some embodiments, methods for sequencing RNA from a sample from a patient are performed in combination with methods for sequencing DNA from a sample from the patient. In some embodiments, RNA-Seq is combined with DNA sequencing. In some embodiments, RNA- Seq complements the results of DNA sequencing. In some embodiments, RNA-Seq determines whether a variant increases the risk of a disease or disorder in a patient as realed to a clinical outcome. In some embodiments, RNA-Seq determines whether gene expression profile increases the risk of a disease or disorder in a patient as realed to a clinical outcome. In some embodiments, RNA-Seq determines whether gene expression profile corresponds to a disease or disorder in a patient. In some embodiments, RNA-Seq is used to generate gene expression data as described herein. In some embodiments, RNA-Seq is used to generate gene expression data predictive of a future diagnosis. In some embodiments, RNA-Seq is used for prediagnostic screening. In some embodiments, RNA-Seq is used for early disease detection.

[0090]

[0071] In some embodiments, methods for sequencing RNA are used to generate gene expression data as described herein. In some embodiments, gene expression data comprises: (i) transcriptomic RNA sequencing data; (ii) RNA expression levels of genes; or (iii) RNA expression levels of genes in a gene set or gene module capable of classifying the disease or disorder of a patient; (iii) RNA expression levels of genes in one or more gene modules capable of classifying the disease or disorder of a patient; or (iv) RNA expression levels of genes in one or more gene modules associated with a molecular endotype associated with a disease or disorder. In some embodiments, gene expression data comprises transcriptomic RNA sequencing data. In some embodiments, gene expression data comprises RNA expression levels of genes. In some embodiments, gene expression data comprises RNA expression levels of genes in a gene set or gene module capable of classifying the disease statedisease or disorder of a patient. In some embodiments, gene expression data comprises RNA expression levels of genes in one or more gene modules capable of classifying the disease or disorder of a patient. In some embodiments, gene expression data comprises RNA expression levels of genes in one or more gene modules associated with a molecular endotype associated with a disease or disorder.

[0091]

[0072] Provided herein are methods for sequencing RNA to generate gene expression data. In some embodiments, gene expression data is indicative of gene expression levels and / or gene expression patterns. In some embodiments, analysis of gene expression data results in the identification of similar gene expression profiles. In some embodiments, gene expression profiles are patterns of gene expression identified from gene expression data. In some embodiments, analysis of gene expression data for one or more genes results in the identification of similar expression profiles for one or more genes. In some embodiments, the gene expression profiles are associated with a disease or disorder. In some embodiments, the gene expression data is associated with a disease or disorder.

[0092]

[0073] In some embodiments, RNA-Seq is used to generate gene expression data associated with a molecular endotype as described herein. In some embodiments, the molecular endotype is indicative of: (i) a subtype of the disease or disorder, (ii) a cellular pathways related to the disease or disorder, (iii) a disease susceptibility of the patient to the disease or disorder; (iv) a treatment with the highest probability of success for the patient; or combinations thereof. qPCR

[0093]

[0074] In some embodiments, systems, compositions, methods, kits, and solutions described herein comprise a quantitative polymerase chain reaction (qPCR). In some embodiments, methods and systems described herein comprise the use of a qPCR to generate gene expression data from a biological sample from a patient. In some embodiments, methods and systems described herein comprise the use of qPCR to generate gene expression data that is indicative of a disease or disorder. In some embodiments, methods and systems described herein comprise the use of qPCR to generate gene expression data from a biological sample from a patient to identify a disease or disorder in a patient.

[0094]

[0075] In some embodiments, a qPCR comprises measuring the enrichment of a nucleic acid sequence in a biological sample. In some embodiments, the qPCR comprises measuring the concentration of a nucleic acid (e.g., DNA or RNA). In some embodiments, the qPCR comprises measuring the concentration of a DNA. In some embodiments, the qPCR comprises measuring the concentration of a RNA. In some embodiments, the qPCR measures the concentration of a nucleic acid sequence of interest in a sample. In some embodiments, the concentration of the nucleic acid sequence of interest is gene expression data.

[0095]

[0076] In some embodiments, systems, compositions, methods, kits, and solutions described herein comprise a PCR, quantitative polymerase chain reaction (qPCR), real-Time PCR (qPCR), digital PCT, multiplexed qPCR, or combinations thereof, to generate gene expression data from a biological sample from a patient. In some embodiments, the qPCR comprises the synthesis of a nucleic acid sequence. In some embodiments, the nucleic acid as described herein is a target nucleic acid. In some embodiments, the nucleic acid from a biological sample as described herein is a target nucleic acid. In some embodiments, the nucleic acid sequence as described herein is a target nucleic acid sequence. In some embodiments, the nucleic acid sequence from a biological sample as described herein is a target nucleic acid sequence.

[0096]

[0077] In some embodiments, teal-Time PCR (qPCR) is a process of monitoring a PCR reaction by recording the fluorescence generated at the end of amplification each cycle. In fluorescently multiplexed PCR, detection of multiple target nucleic acid sequences in a single reaction is accomplished by associating each nucleic acid target with a distinct fluorescent tag. In some embodiments, qPCR comprises a precise method for measuring multiple reporter signals during multiplex qPCR reactions.

[0097]

[0078] In some embodiments, systems, compositions, methods, kits, and solutions described herein comprise fluorometric signal engineering for multiplexed qPCR. In some embodiments, methods described herein comprise an amplification signal generated by a polymerase chain reaction (PCR). In some embodiments, the PCR reaction comprises a sample and a reagent mixture for amplifying a nucleic acid from the sample. In some embodiments, the PCR reaction comprises a biological sample comprising a nucleic acid and a reagent mixture for amplifying the nucleic acid. In some embodiments, the PCR reaction comprises a biological sample comprising a target nucleic acid and a reagent mixture for amplifying the target nucleic acid. In some embodiments, the PCR reaction comprises a sample, a buffer solution, a reagent mixture comprising polymerases, primers, nucleotides, dNTPS, and the like. In some embodiments, the PCR reaction comprises a sample comprising a plurality of target nucleic acids, at least a first set of paired amplification oligomers configured to amplify at least a first target nucleic acid sequence, at least a second set of paired amplification oligomers configured to amplify at least a second target nucleic acid sequence, at least a first detectable probe configured to anneal to at least the first target nucleic acid sequence, and at least a second detectable probe configured to anneal to at least the second target nucleic acid sequence. In some embodiments, the at least the first set of paired amplification oligomers and at least the first detectable probe is at a different concentration than at least the second set of paired amplification oligomers and at least the second detectable probe.

[0098]

[0079] In some embodiments, the PCR reaction comprises amplifying the first target nucleic acid sequence. In some embodiments, the PCR reaction comprises amplifying the first target nucleic acid sequence with a polymerase having 5 ’ to 3 ’ exonuclease activity in the presence of at least the first target nucleic acid sequence to generate at least a first detectable signal, and in the presence of at least the second target nucleic acid sequence to generate at least a second detectable signal, and wherein the amplifying step causes at least the first detectable probe bound to at least the first target nucleic acid and at least the second detectable probe bound to at least the second target nucleic acid to be degraded by the polymerase and permitting generation of at least the first and at least the second detectable signals.

[0099]

[0080] In some embodiments, the PCR reaction comprises measuring the intensity of at least the first detectable signal and at least the second detectable signal upon initiating of the amplification reaction, measuring the intensity of at least the first detectable signal and at least the second detectable signal upon completion of the amplification reaction, and determining a reaction cycle at which the amplification signal first satisfies an amplification criterion. In some embodiments, the PCR reaction comprises detecting signal peak amounts in the signal, and identifying a magnitude of the amplification signal. In some embodiments, the PCR reaction comprises comparing the reaction cycle and the magnitude of the amplification signal and determining the presence or absence of at least the first target nucleic acid sequence, and the presence or absence of at least the second target nucleic acid sequence in the sample.

[0100]

[0081] In some embodiments, at least the first set of paired amplification oligomers comprises at least a first forward amplification oligomer and at least a first reverse amplification oligomer. In some embodiments, the at leastthe second set of paired amplification oligomers comprises at least a second forward amplification oligomers and at least a second reverse amplification oligomer. In some embodiments, the at least the first detectable probe is configured to anneal to at least the first target nucleic acid sequence. In some embodiments, the at least the second detectable probe is configured to anneal to at least the second target nucleic acid sequence. In some embodiments, the amplification is a PCR. In some embodiments, the PCR is a qPCR.

[0101]

[0082] In some embodiments, the qPCR comprises a mixture comprising: a) a buffer; b) a salt; c) a set of dNTPs; and d) an enzyme. In some embodiments, the salt is selected from the group consisting of : (a) magnesium chloride; (b) sodium chloride; (c) potassium chloride; or (d) sodium citrate. In some embodiments, the set of dNTPs is selected from the group consisting of: Set A comprising deoxyadenosine triphosphate (dATP), deoxy cytidine triphosphate (dCTP), deoxyguanosine triphosphate (dGTP), deoxythymidine triphosphate (dTTP); and Set B comprising adenosine triphosphate (ATP), cytidine triphosphate (CTP), guanosine triphosphate (GTP) and uridine triphosphate (UTP). In some embodiments, said enzyme is selected from the group consisting of : (a) a thermostable DNA polymerase; (b) a reverse transcriptase; and (c) a RNA polymerase.

[0102]

[0083] In some embodiments, the first detectable probe and at least the second detectable probe are each independently selected from the group consisting of: a dye; a fluorescent molecule; a chemiluminescent label; and a combination thereof. In some embodiments the at least the first detectable probe and at least the second detectable probe is a dye. In some embodiments the at least the first detectable probe and at least the second detectable probe is a fluorescent molecule. In some embodiments the at least the first detectable probe and at least the second detectable probe is a chemiluminescent label. In some embodiments, the method further comprises during said amplification reaction, releasing said dye from said detection probe, thereby generating said at least a first signal and said at least a second signal. In some embodiments, the mixture comprises primers and probes for at least five nucleic acid targets.

[0103] Amplification Reagents / Components

[0104]

[0084] In some embodiments, systems, compositions, methods, kits, and solutionsdescribed herein comprise a reagent or component for amplifying a nucleic acid. Non-limiting examples of reagents for amplifying a nucleic acid comprise polymerases, primers, and nucleotides. In some embodiments, systems comprise reagents for nucleic acid amplification in a sample. In some embodiments, nucleic acid amplification improves at least one of sensitivity, specificity, or accuracy of the assay. In some embodiments, nucleic acid amplification is isothermal nucleic acid amplification, providing for the use of the system or system in remote regions or low resource settings without specialized equipment for amplification. In some embodiments, amplification of the nucleic acid increases the concentration of the nucleic acid in the sample. In some embodiments, methods and systems described herein comprise labeling the nucleic acid with fluorescent labels or detectable tags. In some embodiments, labeling the nucleic acid comprises the incorporation of nucleotide labels or fluorescent labels such as fluorescently labeled nucleotides during synthesis. In some embodiments, labeling the nucleic acid comprises the incorporation of fluorescently labeled nucleotides. In some embodiments, labeling the nucleic acid comprises the incorporation of detectable tags for nucleotide detection. In some embodiments, fluorescent labels or detectable tags generate a detectable signal. In some embodiments, a detectable signal is measured with an appropriate detection device (e.g., a fluorescent light reader, a visible light reader).

[0105]

[0085] In some embodiments, the reagents for nucleic acid amplification comprise a recombinase, a primer, an oligonucleotide primer, an activator, a deoxynucleoside triphosphate (dNTP), a ribonucleoside triphosphate (rNTP), a single-stranded DNA binding (SSB) protein, Rnase inhibitor, water, a polymerase, reverse transcriptase mix, or a combination thereof that is suitable for an amplification reaction. Non-limiting examples of amplification reactions are transcription mediated amplification (TMA), helicase dependent amplification (HD A), or circular helicase dependent amplification (cHDA), strand displacement amplification (SDA), recombinase polymerase amplification (RPA), loop mediated amplification (LAMP), exponential amplification reaction (EXPAR), rolling circle amplification (RCA), ligase chain reaction (LCR), simple method amplifying RNA targets (SMART), single primer isothermal amplification (SPIA), multiple displacement amplification (MDA), nucleic acid sequence based amplification (NASBA), hinge-initiated primer-dependent amplification of nucleic acids (HIP), nicking enzyme amplification reaction (NEAR), and improved multiple displacement amplification (IMDA).

[0106]

[0086] Such amplification reactions, in some embodiments, are also used in combination with reverse transcription of an RNA of interest. Accordingly, also provided herein are reagents for both the reverse transcription and amplification of nucleic acids. In some embodiments, systems, compositions, methods, kits, and solutions comprise 0.01 pL, 0.02 pL, 0.03 pL, 0.04 pL, 0.05 pL, 0.06 pL, 0.07 pL, 0.08 pL, 0.09 pL, 0.1 pL, 0.2 pL, 0.3 pL, 0.4 pL, 0.5 pL, 0.6 pL, 0.7 pL, 0.8 pL, 0.9 pL, 1 pL, 2 pL, 3 pL, 4 pL, 5 pL, 6 pL, 7 pL, 8 pL, 9 pL, 10 pL, 20 pL, 30 pL, 40 pL, 50 pL, 60 pL, 70 pL, 80 pL, 90 pL, 100 pL, 150 pL, 200 pL, 250 pL, 300 pL, 350 pL, 400 pL, 450 pL, 500 pL, or more of each amplification described herein. In some embodiments, systems, compositions, methods, kits, and solutions comprise 1 nM, 2 nM, 3 nM, 4 nM, 5 nM, 6 nM, 7 nM, 8 nM, 9 nM, 10 nM, 20 nM, 30 nM, 40 nM, 50 nM, 60 nM, 70 nM, 80 nM, 90 nM, 100 nM, 150 nM, 200 nM, 250 nM, 300 nM, 350 nM, 400 nM, 450 nM, 500 nM, or more of each amplification reagent as described herein. In some embodiments, systems, compositions, methods, kits, and solutions comprise 1 pM, 2 pM, 3 pM, 4 pM, 5 pM, 6 pM, 7 pM, 8 pM, 9 pM, 10 pM, 20 pM, 30 pM, 40 pM, 50 pM, 60 pM, 70 pM, 80 pM, 90 pM, 100 pM, 150 pM, 200 pM, 250 pM, 300 pM, 350 pM, 400 pM, 450 pM, 500 pM, or more of each amplification reagent as described herein. In some embodiments, systems, compositions, methods, kits, and solutions comprise 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 20 mM, 30 mM, 40 mM, 50 mM, 60 mM, 70 mM, 80 mM, 90 mM, 100 mM, 150 mM, 200 mM, 250 mM, 300 mM, 350 mM, 400 mM, 450 mM, 500 mM, or more of each amplification reagent as described herein.

[0107]

[0087] In some embodiments, methods and systems described herein comprise a PCR tube, a PCR well or a PCR plate. In some embodiments, the wells of the PCR plate are pre-aliquoted with the reagent for amplifying a nucleic acid. In some embodiments, a user thus adds the biological sample of interest to a well of the pre-aliquoted PCR plate and measure for a detectable signal with a fluorescent light reader or a visible light reader.

[0108]

[0088] In some embodiments, systems comprise a PCR plate and a support medium. In some embodiments, nucleic acid amplification is performed in a nucleic acid amplification region on the support medium. Alternatively, or in combination, the nucleic acid amplification is performed in a reagent chamber, and the resulting sample is applied to the support medium.

[0109]

[0089] Often, the nucleic acid amplification is performed for no greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, or 60 minutes, or any value 1 to 60 minutes. In some embodiments, the amplification reaction is performed for 1 to 60, 5 to 55, 10 to 50, 15 to 45, 20 to 40, or 25 to 35 minutes. In some embodiments, the amplification reaction is performed at a temperature of around 20-45°C. In some embodiments, the amplification reaction is performed at a temperature no greater than 20°C, 25°C, 30°C, 35°C, 37°C, 40°C, 45°C, 50°C, 55°C, 60°C, 65°C, or any value 20 °C to 65 °C. In some embodiments, the amplification reaction is performed at a temperature of at least 20°C, 25°C, 30°C, 35°C, 37°C, 40°C, 45°C, 50°C, 55°C, 60°C, 65°C or any value 20 °C to 65 °C. In some embodiments, the amplification reaction is performed at a temperature of 20°C to 45°C, 25°C to 40°C, 30°C to 40°C, 35°C to 40°C, 40°C to 45°C, 45°C to 50°C, 50°C to 55°C, 55°C to 60°C, 55°C to 65°C, or 60°C to 65°C.

[0110]

[0090] In some embodiments, methods and systems described herein comprise primers for amplifying a nucleic acid to produce an amplification product. The compositions for amplification of nucleic acids and methods of use thereof, as described herein, are compatible with any of the methods disclosed herein including methods of assaying a nucleic acid.

[0111]

[0091] In some embodiments, methods and systems described herein comprise a PCR, qPCR, digital PCT, multiplexed qPCR, or combinations thereof, to generate gene expression data from a biological sample from a patient.

[0112] Samples

[0113]

[0092] In some embodiments, a sample is isolated from a patient. In some embodiments, sample is an isolated biological sample from a patient. In some embodiments, isolating a biological sample from a patient comprises techniques known in the art for the isolation of a biological sample as described herein. In some embodiments, the isolated biological sample is selected from a group consisting of: a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a blood sample, a tissue sample, and a purified cell sample.

[0114]

[0093] Various sample types are consistent with the present disclosure. In some embodiments, the samples are isolated from a patient for identification of a disease or disorder. In some embodiments, the identification of a disease or disorder comprises identification of the progression of a disease or disorder or a susceptibility thereof of the patient. In some embodiments, the identification of a disease or disorder as described herein comprises the assaying of an isolated biological sample from a patient. In some embodiments, the identification of a disease or disorder as described herein comprises assaying a sample to generate a dataset comprising gene expression data. In some embodiments, the identification of a disease or disorder as described herein comprises assaying for phenotyping, genotyping, or determining patient ancestry. Generally, a sample from a patient is obtained for testing a presence of a disease or disorder, a susceptibility of a patient to a disease or disorder, a progression of a disease or disorder, a response to or effectiveness of a treatment administered to the patient with a disease or disorder, or combinations thereof.

[0115]

[0094] In some embodiments, the sample comprises nucleic acids associated to pathway conditions or pathway systems that are not conducive to cell survival, tissue survival, systemic survival, or organism survival. In some embodiments, the sample comprises nucleic acids associated to a disease or disorder as described herein.

[0095] In some embodiments, the sample comprises a cell comprising pathway conditions or pathway systems that are not conducive to cell survival, tissue survival, systemic survival, or organism survival. In some embodiments, the sample comprises cells and / or cell pathway conditions associated to a disease or disorder as described herein.

[0116]

[0096] In some embodiments, samples are used for diagnosing a disease. In some embodiments, the disease is a disease or disorder as described herein.

[0117]

[0097] In some embodiments, a sample is used for identifying a disease status. For example, a sample is any sample described herein, and is obtained from a patient for use in identifying a disease status of a patient. In some embodiments, the disease is a systemic disease or disorder. In some embodiments, the disease is a chronic disease or disorder. In some embodiments, the disease is an inflammatory disease or disorder. In some embodiments, the disease is a noninflammatory disease or disorder. In some embodiments, the disease is an autoimmune disease or disorder. In some embodiments, the disease is an arthritis disease or disorder. In some embodiments, the disease is a lupus disease or disorder. In some embodiments, a method comprises obtaining a sample from a patient; and identifying a disease status of the patient. In some embodiments, the sample from a patient is a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a blood sample, a tissue sample, a purified cell sample, or combinations thereof.

[0118]

[0098] In some embodiments, the sample comprises a blood sample, a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, an isolated peripheral blood mononuclear cell (PBMCs) sample, a tissue sample, a tissue biopsy sample, purified cell sample, a nasal fluid sample, a saliva sample, a urine sample, a stool sample, or combinations thereof.

[0119]

[0099] In some embodiments, the sample comprises a blood sample, a whole blood sample (WB), a peripheral blood mononuclear cell (PBMCs) sample, a tissue sample, a purified cell sample, any derivative thereof, or combinations thereof. In some instances, the blood sample is a whole blood sample or a PBMC sample. In some instances, the blood sample comprises blood cells, serum, plasma, or any combination thereof. In some embodiments, the sample is a biological sample. In some embodiments, the biological sample is a reference biological sample. In some embodiments, the biological sample is an isolated biological sample.

[0120]

[0100] In some embodiments, the isolated biological sample is selected from: a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a blood sample, a tissue sample, a purified cell sample, or combinations thereof.

[0101] In some embodiments, the biological sample is selected from: a blood sample, a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a tissue sample, a tissue biopsy sample, a skin biopsy sample, a purified cell sample, any derivative thereof, or combinations thereof. In some embodiments, the biological sample comprises a blood sample, or any derivative thereof. In some embodiments, the biological sample comprises a whole blood sample, or any derivative thereof. In some embodiments, the biological sample comprises a PBMC sample, or any derivative thereof. In some embodiments, the biological sample comprises a tissue sample, or any derivative thereof. In some embodiments, the biological sample comprises a tissue biopsy sample, or any derivative thereof. In some embodiments, the biological sample comprises a skin biopsy sample, or any derivative thereof. In some embodiments, the biological sample comprises a purified cell sample, or any derivative thereof. In some embodiments, the biological sample is an isolated biological sample.

[0121]

[0102] In some embodiments, isolating a biological sample from a patient comprises techniques known in the art for the isolation of a biological sample as described herein. To isolate a blood sample, various techniques may be used, e.g., a syringe or other vacuum suction device. A blood sample can 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 isolated, or longer if frozen. When isolating a sample from a patient (e.g., blood sample), the amount can vary depending upon patient 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 isolated. In some embodiments, 1-50, 2-40, 3-30, or 4- 20 pL of sample is isolated. 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 isolated.

[0122]

[0103] In some embodiments, a sample comprises a nucleic acid. In some embodiments, the nucleic acid is from a gene with a mutation associated with a disease or disorder as described herein. In some embodiments, the nucleic acid is from a gene whose expression / overexpression / underexpression is associated with a disease or disorder as described herein. In some embodiments, the nucleic acid is encoded by a nucleic acid sequence associated with a disease or disorder as described herein. In some embodiments, the nucleic acid is an RNA or a complementary DNA synthesized from a nucleic acid (RNA) from at least one gene associated with a disease or disorder as described herein. In some embodiments, the nucleic acid is from a genomic locus, a transcribed mRNA, or a reverse transcribed mRNA, a DNA amplicon or a cDNA from at least one gene associated with a disease or disorder as described herein.

[0104] Provided herein are nucleic acids in the sample which are encoded by a nucleic acid sequence. In some embodiments, the nucleic acid sequence from a biological sample as described herein is a target nucleic acid sequence.

[0123]

[0105] In some embodiments, a sample comprises a nucleic acid sequence. In some embodiments, the assaying of the sample generates a dataset comprising gene expression data. In some embodiments, the sample is the isolated biological sample from a patient. In some embodiments, the isolated biological sample from the patient comprises a nucleic acid sequence. In some embodiments, the assaying of the biological sample from the patient generates a dataset comprising gene expression data. In some embodiments, the assaying of the biological sample from the patient comprises measuring a concentration of a nucleic acid sequence. In some embodiments, the assaying of the biological sample from the patient comprises sequencing a nucleic acid sequence from the biological sample or an amplicon thereof. In some embodiments, the assaying of the biological sample from the patient comprises analyzing the transcriptome of the biological sample by sequencing a nucleic acid sequence (e.g., RNA) from the biological sample or an amplicon thereof. In some embodiments, the assaying of the biological sample from the patient comprises analyzing the transcriptome of the biological sample by sequencing a complementary DNA (e.g., cDNA) synthesized from a nucleic acid sequence (e.g., RNA) from the biological sample or an amplicon thereof. In some embodiments, the assaying of the biological sample from the patient comprises measuring the enrichment of the nucleic acid sequence in the biological sample or an amplicon thereof. In some embodiments, the assaying of the biological sample from the patient comprises a microarray assay, an RNA-Seq assay, and / or a quantitative polymerase chain reaction (qPCR).

[0124]

[0106] In some embodiments, a sample comprises a nucleic acid at a concentration of less than 1 nM, less than 2 nM, less than 3 nM, less than 4 nM, less than 5 nM, less than 6 nM, less than 7 nM, less than 8 nM, less than 9 nM, less than 10 nM, less than 20 nM, less than 30 nM, less than 40 nM, less than 50 nM, less than 60 nM, less than 70 nM, less than 80 nM, less than 90 nM, less than 100 nM, less than 200 nM, less than 300 nM, less than 400 nM, less than 500 nM, less than 600 nM, less than 700 nM, less than 800 nM, less than 900 nM, less than 1 pM, less than 2 pM, less than 3 pM, less than 4 pM, less than 5 pM, less than 6 pM, less than 7 pM, less than 8 pM, less than 9 pM, less than 10 pM, less than 100 pM, or less than 1 mM. In some embodiments, the sample comprises a nucleic acid at a concentration of 1 nM to 2 nM, 2 nM to 3 nM, 3 nM to 4 nM, 4 nM to 5 nM, 5 nM to 6 nM, 6 nM to 7 nM, 7 nM to 8 nM, 8 nM to 9 nM, 9 nM to 10 nM, 10 nM to 20 nM, 20 nM to 30 nM, 30 nM to 40 nM, 40 nM to 50 nM, 50 nM to 60 nM, 60 nM to 70 nM, 70 nM to 80 nM, 80 nM to 90 nM, 90 nM to 100 nM, 100 nM to 200 nM, 200 nM to 300 nM, 300 nM to 400 nM, 400 nM to 500 nM, 500 nM to 600 nM, 600 nM to 700 nM, 700 nM to 800 nM, 800 nM to 900 nM, 900 nM to 1 pM, 1 pM to 2 pM, 2 pM to 3 pM, 3 pM to 4 pM, 4 pM to 5 pM, 5 pM to 6 pM, 6 pM to 7 pM, 7 pM to 8 pM, 8 pM to 9 pM, 9 pM to 10 pM, 10 pM to 100 pM, 100 pM to 1 mM, 1 nM to 10 nM, 1 nM to 100 nM, 1 nM to 1 pM, 1 nM to 10 pM, 1 nM to 100 pM, 1 nM to 1 mM, 10 nM to 100 nM, 10 nM to 1 pM, 10 nM to 10 pM, 10 nM to 100 pM, 10 nM to 1 mM, 100 nM to 1 pM, 100 nM to 10 pM, 100 nM to 100 pM, 100 nM to 1 mM, 1 pM to 10 pM, 1 pM to 100 pM, 1 pM to 1 mM, 10 pM to 100 pM, 10 pM to 1 mM, or 100 pM to 1 mM. In some embodiments, the sample comprises a nucleic acid at a concentration of 20 nM to 200 pM, 50 nM to 100 pM, 200 nM to 50 pM, 500 nM to 20 pM, or 2 pM to 10 pM. In some embodiments, the sample comprises an amplicon of a nucleic acid. In some embodiments, the nucleic acid is not present in the sample.

[0125]

[0107] In some embodiments, the sample is a biological sample, an environmental sample, or a combination thereof. Non-limiting examples of biological samples are blood, serum, plasma, saliva, urine, mucosal sample, peritoneal sample, cerebrospinal fluid, gastric secretions, nasal secretions, sputum, pharyngeal exudates, urethral or vaginal secretions, an exudate, an effusion, and a tissue sample (e.g., a biopsy sample). In some embodiments, a tissue sample from a patient is dissociated or liquified prior to application to detection system of the present disclosure. Nonlimiting examples of environmental samples are soil, air, or water. In some embodiments, an environmental sample is taken as a swab from a surface of interest or taken directly from the surface of interest.

[0126]

[0108] In some embodiments, the sample is a raw (unprocessed, unedited, unmodified) sample. In some embodiments, raw samples are assayed as described herein. In some embodiments, the sample is diluted with a buffer or a fluid or concentrated prior to assaying as described herein. Sometimes, the sample comprises no more 20 pl of buffer or fluid. The sample, in some embodiments, is compriseed in no more than 0.01, 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35 40, 45, 50, 55, 60, 65, 70, 75, 80, 90, 100, 200, 300, 400, 500 pl, or any of value 0.01 pl to 500 pl, 0.1 pL to 100 pL, or more preferably 1 pL to 50 pL of buffer or fluid. Sometimes, the sample is compriseed in more than 500 pl. In some embodiments, the compositions, systems, and methods disclosed herein are compatible with the buffers or fluid disclosed herein.

[0127]

[0109] In some embodiments, the sample is taken from a single-cell eukaryotic organism; a plant or a plant cell; an algal cell; a fungal cell; an animal cell, tissue, or organ; a cell, tissue, or organ from an invertebrate animal; a cell, tissue, fluid, or organ from a vertebrate animal such as fish, amphibian, reptile, bird, and mammal; a cell, tissue, fluid, or organ from a mammal such as a human, a non-human primate, an ungulate, a feline, a bovine, an ovine, and a caprine. In some embodiments, the sample is taken from nematodes, protozoans, helminths, or malarial parasites. In some embodiments, the sample comprises nucleic acids from a cell lysate from a eukaryotic cell, a mammalian cell, a human cell, a prokaryotic cell, or a plant cell. In some embodiments, the sample comprises nucleic acids expressed from a cell.

[0128] Gene Module

[0129] [HO] In some embodiments, methods described herein comprise gene modules. In some embodiments, methods described herein comprise gene modules comprising at least 2 genes. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, at least 9 genes, at least 10 genes, at least 11 genes, at least 12 genes, at least 13 genes, at least 14 genes, at least 15 genes, at least 16 genes, at least 17 genes, at least 18 genes, at least 19 genes, at least 20 genes, at least 21 genes, at least 22 genes, at least 23 genes, at least 24 genes, at least 25 genes, at least 26 genes, at least 27 genes, at least 28 genes, at least 29 genes, at least 30 genes, at least 31 genes, at least 32 genes, at least 33 genes, at least 34 genes, at least 35 genes, at least 36 genes, at least 37 genes, at least 38 genes, at least 39 genes, at least 40 genes, at least 41 genes, at least 42 genes, at least 43 genes, at least 44 genes, at least 45 genes, at least 46 genes, at least 47 genes, at least 48 genes, at least 49 genes, or at least 50 genes. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 5 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, or at least 50 genes. In some embodiments, the gene modules described herein comprise 2 or more genes, 5 or more genes, 10 or more genes, 15 or more genes, 20 or more genes, 25 or more genes, 30 or more genes, 35 or more genes, 40 or more genes, 45 or more genes, or 50 or more genes. In some embodiments, the gene modules described herein comprise about 2 genes, about 5 genes, about 10 genes, about 15 genes, about 20 genes, about 25 genes, about 30 genes, about 35 genes, about 40 genes, about 45 genes, or about 50 genes. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 5 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, or at least 50 genes associated with a disease or disorder described herein. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 5 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, or at least 50 genes associated with a molecular endotype described herein. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 5 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, or at least 50 genes associated with a biological pathway. In some embodiments, the gene modules described herein comprise at least 2 genes, at least 5 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 30 genes, at least 35 genes, at least 40 genes, at least 45 genes, or at least 50 genes associated with a phenotype. In some embodiments, the phenotype is associated with a disease or disorder, a progression of a disease or disorder, a deterioration, a disability, an organ involvement, a medication response, a treatment response, a treatment target, a molecular endotype, or any combination thereof.

[0130] [Hl] In some embodiments, methods described herein comprise gene modules comprising at least 2 genes associated with a disease or disorder as described herein. In some embodiments, methods described herein comprise gene modules comprising at least 2 genes as described in TABLE 4 In some embodiments, methods described herein comprise gene modules comprising 2 or more genes as described in TABLE 4 In some embodiments, the gene modules described herein comprise the genes described in TABLE 4 In some embodiments, the at least 2 genes as described herein comprise genes described in TABLE 4

[0131]

[0112] In some embodiments, the gene modules described herein comprise genes associated with a disease or disorder as described herein. In some embodiments, the gene modules described herein comprise genes associated with a disease or disorder as shown in FIG. 1-FIG. 20. In some embodiments, the gene modules described herein are associated with a disease or disorder, treatment, process, or cell type as shown in FIG. 1-FIG. 20. In some embodiments, a disease or disorder is associated with one or more gene modules as described herein. In some embodiments, a molecular endotype is associated with one or more gene modules as described herein. In some embodiments, a biological pathway is associated with one or more gene modules as described herein. In some embodiments, a phenotype is associated with one or more gene modules as described herein. In some embodiments, one or more gene modules as described herein are associated with the disease or disorder, the progression of a disease or disorder, the deterioration, the disability, the organ involvement, the medication response, the treatment response, the treatment target, the molecular endotype, or any combination thereof.

[0132]

[0113] In some embodiments, the gene modules as described herein are significant gene modules. In some embodiments, the significant gene modules comprise a minimum number of genes. In some embodiments, the minimum number of genes as described herein is considered a threshold minimum size. In some embodiments, the threshold minimum size is about 2 genes to about 50 genes. In some embodiments, a plurality of gene modules comprise the gene modules described herein. In some embodiments, the plurality of gene modules comprise one or more gene modules described herein. In some embodiments, the plurality of gene modules comprise 2 or more gene modules. In some embodiments, the plurality of gene modules comprise 5 or more gene modules. In some embodiments, the plurality of gene modules comprise 10 or more gene modules. In some embodiments, the plurality of gene modules comprise 15 or more gene modules. In some embodiments, the plurality of gene modules comprise 20 or more gene modules. In some embodiments, the plurality of gene modules comprise 25 or more gene modules. In some embodiments, the plurality of gene modules comprise 30 or more gene modules. In some embodiments, the plurality of gene modules comprise 35 or more gene modules. In some embodiments, the plurality of gene modules comprise 40 or more gene modules. In some embodiments, the plurality of gene modules comprise 45 or more gene modules. In some embodiments, the plurality of gene modules comprise 50 or more gene modules. In some embodiments, the plurality of gene modules comprise 55 or more gene modules. In some embodiments, the plurality of gene modules comprise 60 or more gene modules. In some embodiments, the plurality of gene modules comprise 65 or more gene modules. In some embodiments, the plurality of gene modules comprise 70 or more gene modules. In some embodiments, the plurality of gene modules comprise 75 or more gene modules. In some embodiments, the plurality of gene modules comprise 80 or more gene modules.

[0133]

[0114] In some embodiments, the plurality of gene modules comprises at least 2 to at least 80 gene modules. In some embodiments, the plurality of gene modules comprises at least 5 to at least 80 gene modules. In some embodiments, the plurality of gene modules comprises at least 10 to at least 80 gene modules. In some embodiments, wherein the plurality of gene modules comprises gene modules that are most strongly correlated with the one or more sample traits as described herein. In some embodiments, at least 2 to at least 80 gene modules are most strongly correlated with the one or more sample traits as described herein. In some embodiments, at least 5 to at least 80 gene modules are most strongly correlated with the one or more sample traits as described herein. In some embodiments, at least 10 to at least 80 gene modules are most strongly correlated with the one or more sample traits as described herein.

[0134]

[0115] In some embodiments, the methods described herein comprise gene modules comprising genes as described herein. In some embodiments, the gene modules comprise genes of the gene set. In some embodiments, the gene modules are significant gene modules as described herein. In some embodiments, the gene modules described herein comprise genes, as shown in TABLE 4 In some embodiments, the significant gene modules described herein comprise genes, as shown in TABLE 4

[0116] In some embodiments, the genes listed within each Table form a significant gene module. In some embodiments, genes listed within different Tables form different gene modules. In some embodiments, the genes listed within TABLE 4 form a gene module. In some embodiments, the genes listed within TABLE 4 form a significant gene module. In some embodiments, the genes listed within a different Table (e.g., TABLE 4) form a different gene module. In some embodiments, the genes listed within a different Table (e.g., TABLE 4) form a different significant gene module. In some embodiments, the genes listed in each of one or more Tables (e.g., TABLE 4) form the gene module. In some embodiments, the genes listed in each of one or more Tables (e.g., TABLE 4) form the significant gene module. In some embodiments, the genes listed within a Table (e.g, TABLE 4) form a significant gene module, and the genes listed within a different Table (e.g, TABLE 4) form a different significant gene module.

[0135] I. Methods

[0136]

[0117] Provided herein are methods for disease prediction and uses thereof. In general, methods described herein may be useful in developing targeted personalized treatment for patients with chronic, inflammatory, and / or autoimmune diseases. In some embodiments, methods for treating, preventing, or inhibiting a disease or disorder in a patient comprise identifying subsets of patients or different disease phenotypes as related to gene expression data. In some embodiments, methods for treating, preventing, or inhibiting a disease or disorder in a patient comprise disease prediction of different subsets of patients as related to gene expression data. In some embodiments, methods for predicting the clinical outcome of a patient comprise identifying subsets of patients based on gene expression data. In some embodiments, methods described herein comprise complex data analysis for disease prediction. In some embodiments, disease prediction involves a machine learning model, algorithm, and / or classifier. In some embodiments, disease prediction involves a machine learning model. In some embodiments, disease prediction involves a machine learning algorithm. In some embodiments, disease prediction involves a machine learning classifier. In some embodiments, methods described herein comprise analysis of gene expression data generated from assaying a sample from a patient. In some embodiments, methods describe herein comprise analyzing gene expression data from one or more genes to determine coexpression patterns. In some embodiments, methods described herein comprise analyzing gene expression data from one or more genes to determine one or more genes with similar gene expression patterns. In some embodiments, methods described herein comprise analyzing gene expression data from one or more genes to determine a molecular endotype. In some embodiments, methods described herein comprise analyzing gene expression data to determine a gene set. In some embodiments, methods described herein comprise analyzing gene expression data to determine a gene module. In some embodiments, methods described herein comprise a gene set associated to a disease or disorder. In some embodiments, methods described herein comprise a gene module associated to a disease or disorder.

[0137] II. Methods for Determining a Gene Set

[0138]

[0118] Provided herein are methods for determining a gene set or gene module capable of classifying a disease or disorder of a patient. In some embodiments, analysis of gene expression data results in the categorization of genes as gene sets or gene modules. In some embodiments, analysis of gene expression data results in the identification of similar gene expression profiles. In some embodiments, gene expression profiles are patterns of gene expression identified from gene expression data. In some embodiments, analysis of gene expression data for one or more genes results in the identification of similar expression profiles for one or more genes. In some embodiments, genes with similar expression profiles are categorized into gene sets. In some embodiments, one or more genes with similar expression profiles are categorized into gene sets. In some embodiments, one or more genes with similar expression profiles are categorized into gene modules. In some embodiments, the gene expression profiles are associated with a disease or disorder. In some embodiments, the gene expression data is associated with a disease or disorder. In some embodiments, the gene modules are associated with a disease or disorder. In some embodiments, the gene modules classify the disease or disorder in a patient. In some embodiments, the gene expression data generated from assaying a biological sample from a patient is compared to gene modules. In some embodiments, the gene expression data generated from assaying a biological sample from a patient identifies the disease or disorder in a patient. In some embodiments, the gene expression data generated from assaying a biological sample from a patient is compared to the gene modules, thus identifying the presence or absence of a disease or disorder in a patient. In some embodiments, the gene expression data generated from assaying a biological sample from a patient is compared to gene modules, thus identifying the immunological state of a patient, the susceptibility of a patient to a disease or disorder, the progression of a disease or disorder of a patient, the rate of deterioration of a patient as related to a disease or disorder, the likelihood that a patient will respond to a particular treatment for a disease or disorder, or combinatios thereof. In some embodiments, the gene expression data generated from assaying a biological sample from a patient is compared to gene modules, thus predicting a clinical outcome of the disease or disorder of the patient. In some embodiments, the gene expression data generated from assaying a biological sample from a patient is used for the identification of a treatment, treatment schedule, or drug that correlates to the best clinical outcome for the patient. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different subsets of patients corresponding to a different disease phenotype. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different subsets of patients corresponding to a different clinical outcome. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different subsets of patients corresponding to a different treatment. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different clusters of patients corresponding to a different treatment. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different subsets of patients corresponding to a different disease phenotype, with the different disease phenotype requiring a different treatment. In some embodiments, the gene expression data generated from assaying the biological sample from the patient is used to identify different subsets of patients corresponding to a different molecular endotype, with the different molecular endotype requiring a different treatment. In some embodiments, the phenotype is associated with a disease or disorder, a progression of a disease or disorder, a trajectory of a disease or disorder, a deterioration, a disability, an organ involvement, a medication response, a treatment response, a treatment target, a treatment, a treatment recommendation, a molecular endotype, or combinations thereof. In some embodiments, the trajectory of the disease or disorder comprises patient decline comprising slow decline, gradual decline, stair step decline, or rapid decline.

[0139]

[0119] In some embodiments, the methods described herein are methods for generating a data set comprising gene expression data by assaying a biological sample from a patient. In some embodiments, the methods described herein comprise generating gene expression data by measuring the concentration of a nucleic acid in a biological sample. In some embodiments, the methods described herein comprise generating gene expression data by analyzing the transcriptome in a biological sample. In some embodiments, the methods described herein comprise generating gene expression data by sequencing the nucleic acid in a biological sample. In some embodiments, the methods described herein comprise assaying a biological sample from a patient. In some embodiments, the methods described herein result in the identification of a molecular endotype. In some embodiments, the gene expression data from a biological sample from a patient results in the identification of the molecular endotype of the patient. In some embodiments, the molecular endotype is indicative of a subtype of a disease or disorder. In some embodiments, the molecular endotype is indicative of the cellular pathways related to a disease or disorder. In some embodiments, the molecular endotype is indicative of the disease susceptibility of the patient to a disease or disorder. In some embodiments, the molecular endotype is indicative of the treatment with the highest probability of success for a patient. In some embodiments, the treatment with the highest probability of success relates to a treatment that is the most likely to prevent a disease or disorder, reduce the rate of deterioration related to a disease or disorder, reduce or stop the progression of a disease or disorder, reduce or stop the symptoms of the disease or disorder, increase the rate of regeneration of the patient as related to the disease or disorder, or combinations thereof. In some embodiments, the treatment that has the highest probability of success is the most effective treatment.

[0140]

[0120] In some embodiments, assaying a biological sample from a patient as described herein comprises measuring the concentration of a nucleic acid in a biological sample, analzing the transcriptome in a biological sample, sequencing a nucleic acid in a biological sample, or combinations thereof. In some embodiments, assaying a biological sample from a patient as described herein comprises the use of any method for generating gene expression data. In some embodiments, assaying a biological sample from a patient as described herein comprises the use of any method for generating gene expression data that results in the identification of a molecular endotype. In some embodiments, assaying a biological sample from a patient as described herein comprises the use of sequencing, next-generation sequencing (NGS), RNA sequencing (RNA- seq), microarrays, DNA microarrays, RNA microarrays, quantitative PCR (qPCR), or combinations thereof.

[0141]

[0121] Also provided herein are methods for determining a gene set capable of classifying a disease or disorder. In some embodiments, the method for determining a gene set capable of classifying a disease or disorder comprise N genes moduled into gene modules as described herein. In some embodiments, the N genes are selected from the initial gene set or the first gene set based on variation in the gene expression within the plurality of reference biological samples.

[0142]

[0122] In some embodiments, analyzing a data set to select N genes from an initial gene set, where N is an integer number. The data set can comprise gene expression data of genes of the initial gene set, from a plurality of reference biological samples. The plurality of reference biological samples can be obtained or derived from a plurality of reference patients. In some embodiments, analyzing the dataset can comprise obtaining a first gene set from the initial gene set, and selecting the N genes from the first gene set. The first gene set can be a subset of the initial gene set. Each genes of the first gene set can be mapped to at least one known protein. The first gene set can be obtained from the initial gene set by removing genes that cannot be mapped to a known protein. In some embodiments, the genes within the first gene set are protein coding genes. In some embodiments, the mapping is performed using the publicly available R BioMaRt package to query probes for any corresponding HGNC gene symbol mappings. The N genes can be selected from the initial gene set or the first gene set based on variation in the gene expression within the plurality of reference biological samples. In some embodiments, the N genes are N variably expressed genes of the initial gene set or the first gene set or both. In some embodiments, the N genes are N variably expressed genes of the initial gene set. In some embodiments, the N genes are N variably expressed genes of the first gene set. In some embodiments, the N genes are N most variably expressed genes of the initial gene set or the first gene set or both. In some embodiments, the N genes are N most variably expressed genes of the initial gene set. In some embodiments, the N genes are N most variably expressed genes of the first gene set. The variable expression can be based on gene expression in the plurality of reference biological samples. In some embodiments, the genes selected from the initial gene set and / or the first gene set comprisecomprise the N genes. In some embodiments, the genes selected from the initial gene set and / or the first gene set comprise the N genes and an additional gene. In some embodiments, the genes selected from the initial gene set and / or the first gene set comprise the N genes and no additional gene. In some embodiments, the N genes are grouped into a gene module. In some embodiments, the N genes within a gene module correlate to a gene expression profile. In some embodiments, the N genes are grouped into a plurality of gene modules. In some embodiments, one or more gene modules of the plurality of gene modules are correlated with one or more sample traits. In some embodiments, comprisecompriseone or more gene modules of the plurality of gene modules are correlated with one or more sample traits of the plurality of reference patients. In some embodiments, a plurality of significant gene modules are selected from the plurality of gene modules correlated with one or more sample traits. In some embodiments, the plurality of significant gene modules are selected from the plurality of gene modules based on strength of the correlation with one or more sample traits as described herein. In some embodiments, the genes within the plurality of significant gene modules form the gene set capable of classifying the disease or disorder of the patient. In some embodiments, the gene set capable of classifying the disease or disorder of the patient comprises a characteristic gene expression profile. In some embodiments the gene set capable of classifying the disease or disorder of the patient is correlated with sample traits. In some embodiments, the gene expression data set generated from assaying an isolated biological sample comprises gene expression data from the genes within the gene modules. In some embodiments, the gene expression data set generated from assaying the isolated biological sample comprises gene expression data from the genes within the plurality of gene modules. In some embodiments, the gene expression data set generated from assaying the isolated biological sample comprises gene expression data from the genes within the plurality of significant gene modules. In some embodiments, the gene expression data is capable of classifying the disease or disorder of the patient. In some embodiments, the gene expression data of the gene set is capable of classifying the disease or disorder of the patient. In some embodiments, the gene expression data of the genes of the gene set is capable of classifying the disease or disorder of the patient.

[0143]

[0123] In some embodiments, the plurality of gene modules are correlated with one or more sample traits, selecting a plurality of significant gene modules based at least on strength of the correlation, overlapping one or more significant gene modules with one or more gene function signature lists, annotating the one or more significant gene modules with one or more functional characterizations based on sufficient overlap between one or more significant gene modules and the one or more gene function signature lists such that sufficient overlap satisfies overlap of a threshold minimum number of genes, and patients with a disease or disorder are determined to correspond to different patient populations or different treatment populations. In some embodiments, patients in different patient populations receive different treatments for the disease or disorder. In some embodiments, sufficient overlap between one or more significant gene modules and a functional characterization group satisfies overlap of a threshold minimum number of genes between one or more significant gene modules and a functional characterization group. In some embodiments, the threshold minimum number of genes are about 3 genes to about 12 genes. In some embodiments, the overlap is measured by any suitable technique. In some embodiments, the overlap is measured using fisher’s exact test. In some embodiments, the sufficient overlap (e.g., for the threshold minimum number of genes) has a threshold Fisher’s adjusted p value. In some embodiments, the threshold Fisher’s adjusted p value for sufficient overlap can be about < 0.3, about < 0.2, or < 0.1.

[0144]

[0124] In some embodiments, the one or more gene function signature lists are selected from the one or more gene function signature lists in the Tables or Figures.

[0145]

[0125] In some embodiments, the gene function signature lists, the functional characterization groups e.g., categories) within the list, and genes within the functional characterization groups for AMPEL Endotype.32 (Endo.32), AMPEL Ancestry (Anc), AMPEL tissues (Tis), and Biologically Informed Gene Clustering (BIG-C), are provided in Catalina, Michelle D., et al. "Patient ancestry significantly contributes to molecular heterogeneity of systemic lupus erythematosus." JCI insight 5.15 (2020); for GO is publicly available at http: / / geneontology.org / ; for BRETIGEA is provided in McKenzie, Andrew T., et al. "Brain cell type specific gene expression and coexpression network architectures." Scientific reports 8.1 (2018): 1-19; for Hallmark gene sets, KEGG Pathway Database, Reactome signature is publicly available at http: / / www.gsea- msigdb.org / gsea / msigdb / collections.jsp.

[0126] In some embodiments, classifying the disease or disorder of the patient comprises determining whether the patient has a disease or disorder. In some embodiments, classifying the disease or disorder of the patient comprises determining a molecular endotype. In some embodiments, classifying the disease or disorder of the patient comprises determining the patient’s molecular endotype. As described herein, the terms “molecular endotype” and “endotype” may be used interchangeably. In some embodiments, classifying the disease or disorder of the patient comprises determining the patient’s molecular endotype out of a group of endotypes. In some embodiments, classifying the disease or disorder of the patient comprises determining the patient’s molecular endotype out of two or more endotypes. In some embodiments, classifying the disease or disorder of the patient comprises determining the group of endotypes associated with the gene expression data. In some embodiments, classifying the disease or disorder of the patient comprises determining the group of endotypes associated with the disease or disorder. In some embodiments, classifying the disease or disorder of the patient comprises determining the endotypes associated with the gene set. In some embodiments, classifying the disease or disorder of the patient comprises determining the disease endotype distribution within a patient population comprising reference patients.

[0146]

[0127] In some embodiments, classifying the disease or disorder of the patient is associated with the disease endotype distribution within reference patients.

[0147]

[0128] In some embodiments, the patient population comprising reference patients comprises two or more endotypes. In some embodiments, the patient population comprises a plurality of reference patients.

[0148]

[0129] In some embodiments, the plurality of reference patients comprises a first plurality of reference patients having a first endotype of the disease or disorder, a second plurality of reference patients having a second endotype of the disease or disorder, and a third plurality of reference patients having a third endotype of the disease or disorder. In some embodiments, classifying the disease or disorder of the patient comprises classifying whether the patient has the first endotype of the disease or disorder, the second endotype of the disease or disorder, or the third endotype of the disease or disorder.

[0149]

[0130] In some embodiments, the method optionally comprises functionally annotating the plurality of significant gene modules. In some embodiments, the method can optionally comprise functionally annotating the plurality of gene modules. In some embodiments, the group of endotypes comprises all endotypes of the disease or disorder. In some embodiments, the group of endotypes does not comprise all endotypes of the disease or disorder. In some embodiments, the two or more endotypes comprise all endotypes of the disease or disorder. In some embodiments, the two or more endotypes do not comprise all endotypes of the disease or disordercomprise. In some embodiments, the reference patients comprise healthy controls. In some embodiments, the reference patients do not comprise healthy controls. In some embodiments, the comprisemethods described herein comprise the use of a computer. In some embodiments, the methods described herein comprise analyzing data with a computer. In some embodiments, the methods described herein are performed with the use of a computer. In some embodiments, the methods described herein are implemented with the use of a computer.

[0150]

[0131] In some embodiments, the data set comprisecomprises a plurality of individual data sets. In some embodiments, the plurality of individual data sets is obtained from the plurality of reference patients. In some embodiments, a data set is obtained from a refence patient. In some embodiments, an individual data set is obtained from a reference patient. In some embodiments, a data set is obtained from each reference patient. In some embodiments, an individual data set of a plurality of individual data sets is obtained from each reference patient of a plurality of reference patients. In somesome embodiments, different individual data sets are obtained from different reference patients. In some embodiments, an individual data set comprises gene expression data. In some embodiments, an individual data set comprises gene expression data from a reference biological sample. In some embodiments, an individual data set comprises gene expression data from a reference biological sample from a reference patient. In some embodiments, an individual data set comprises reference gene expression data. In some embodiments, a data set comprises reference gene expression data, comprisein some embodiments, the data set comprises gene expression data from the genes of the initial gene set. In some embodiments, each individual data set comprisecomprises gene expression data from a reference biological sample from a reference patient of the plurality of reference patients, of the genes of the initial gene set.

[0151]

[0132] In some embodiments, the genes of the initial gene set are genes, protein coding genes, transcribed genes, or subsets thereof. In somesome embodiments, the genes of the initial gene set are genes, protein coding genes, transcribed genes, or subsets thereof, in the plurality of reference biological samples. In somesome embodiments, genes in the initial gene set are genes, protein coding genes, transcribed genes, or subsets thereof, for which gene expression data from the plurality of reference biological samples is available in the data set. In somesome embodiments, genes in the initial gene set are genes, protein coding genes, transcribed genes, or subsets thereof, for which gene expression data from each reference biological sample of the plurality of reference biological samples is available in the data set. In somesome embodiments, the subsets of genes, protein coding genes, or transcribed genes are obtained by removing genes, protein coding genes, or transcribed genes, respectively. In some embodiments, removing genes as described herein comprises removing genes with low copy number. In some embodiments, removing genes as described herein comprises removing genes that one of skill in the art would want to remove from the subset of genes.

[0152]

[0133] In some embodiments, the N genes are N most variably expressed genes of the initial gene set in the data set. In some embodiments, the N genes are N most variably expressed genes of the first gene set in the data set. In somesome embodiments, the N genes are N most variably expressed genes of the initial gene set and / or the first gene set in the data set. In some embodiments, N most variably expressed genes are selected and grouped. In some emboidmnets, selected and grouped N most variably expressed genes are useful for dimensionality reduction, obtaining high quality data for gene grouping and subsequent analysis, reducing noise from the data, improving speed of computer systems, or combinations thereof.

[0153]

[0134] In somesome embodiments, the N most variably expressed genes are selected using variable expression. In some embodiments, variable expression is measured using row variance. In some embodiments, genes with higher variable expression within the plurality of reference biological samples have higher row variance. In some embodiments, average row variance is calculated. In some embodiments, average row variance is stored as a matrix. In some embodiments, the matrix comprises the averaged gene expressions of each gene. In some embodiments, the matrix comprises the averaged gene expressions of each gene (e.g., initial gene set or the first gene set) as rows. In some embodiments, the matrix comprises the averaged samples (e.g., reference patients / reference biological samples) as columns. In some embodiments, the matrix comprises the averaged gene expressions of each gene as rows, and samples as columns. In some embodiments, the matrix is sorted by decreasing average row variance. In some embodiments, the matrix is sorted and the top N genes are selected to obtain N most variably expressed genes. In some embodiments, the matrix is sorted by decreasing average row variance and the top N genes can be selected, to obtain N most variably expressed genes. In some embodiments, using row variance allows obtaining modules in an unsupervised and statistically non-biased manner. In some embodiments, using row variance allows obtaining modules in an unsupervised and statistically non-biased manner based on statistically significant gene expression data. In some embodiments, the method comprises data sets comprising healthy controls. In some embodiments, the method comprising data sets comprising no healthy controls.

[0154]

[0135] In somesome embodiments, N is about 500 to about 10,000. In some embodiments, N is about 500 to about 10,000, most variably expressed genes of the initial gene set or the first gene set or both. In some embodiments, N is about 500 to about 1,000, 500 to about 2,000, about 500 to about 3,000, about 500 to about 4,000, about 500 to about 4,500, about 500 to about 5,000, about 500 to about 5,500, about 500 to about 6,000, about 500 to about 7,000, about 500 to about 8,000, about 500 to about 9,000, about 500 to about 10,000, 1,000 to about 2,000, about 1,000 to about 3,000, about 1,000 to about 4,000, about 1,000 to about 4,500, about 1,000 to about 5,000, about 1,000 to about 5,500, about 1,000 to about 6,000, about 1,000 to about 7,000, about 1,000 to about 8,000, about 1,000 to about 9,000, about 1,000 to about 10,000, about 2,000 to about 3,000, about 2,000 to about 4,000, about 2,000 to about 4,500, about 2,000 to about 5,000, about 2,000 to about 5,500, about 2,000 to about 6,000, about 2,000 to about 7,000, about 2,000 to about 8,000, about 2,000 to about 9,000, about 2,000 to about 10,000, about 3,000 to about 4,000, about 3,000 to about 4,500, about 3,000 to about 5,000, about 3,000 to about 5,500, about 3,000 to about 6,000, about 3,000 to about 7,000, about 3,000 to about 8,000, about 3,000 to about 9,000, about 3,000 to about 10,000, about 4,000 to about 4,500, about 4,000 to about 5,000, about 4,000 to about 5,500, about 4,000 to about 6,000, about 4,000 to about 7,000, about 4,000 to about 8,000, about 4,000 to about 9,000, about 4,000 to about 10,000, about 4,500 to about 5,000, about 4,500 to about 5,500, about 4,500 to about 6,000, about 4,500 to about 7,000, about 4,500 to about 8,000, about 4,500 to about 9,000, about 4,500 to about 10,000, about 5,000 to about 5,500, about 5,000 to about 6,000, about 5,000 to about 7,000, about 5,000 to about 8,000, about 5,000 to about 9,000, about 5,000 to about 10,000, about 5,500 to about 6,000, about 5,500 to about 7,000, about 5,500 to about 8,000, about 5,500 to about 9,000, about 5,500 to about 10,000, about 6,000 to about 7,000, about 6,000 to about 8,000, about 6,000 to about 9,000, about 6,000 to about 10,000, about 7,000 to about 8,000, about 7,000 to about 9,000, about 7,000 to about 10,000, about 8,000 to about 9,000, about 8,000 to about 10,000, or about 9,000 to about 10,000. In some embodiments, N is about 500 to about 1,000, 500 to about 2,000, about 500 to about 3,000, about 500 to about 4,000, about 500 to about 4,500, about 500 to about 5,000, about 500 to about 5,500, about 500 to about 6,000, about 500 to about 7,000, about 500 to about 8,000, about 500 to about 9,000, about 500 to about 10,000, 1,000 to about 2,000, about 1,000 to about 3,000, about 1,000 to about 4,000, about 1,000 to about 4,500, about 1,000 to about 5,000, about 1,000 to about 5,500, about 1,000 to about 6,000, about 1,000 to about 7,000, about 1,000 to about 8,000, about 1,000 to about 9,000, about 1,000 to about 10,000, about 2,000 to about 3,000, about 2,000 to about 4,000, about 2,000 to about 4,500, about 2,000 to about 5,000, about 2,000 to about 5,500, about 2,000 to about 6,000, about 2,000 to about 7,000, about 2,000 to about 8,000, about 2,000 to about 9,000, about 2,000 to about 10,000, about 3,000 to about 4,000, about 3,000 to about 4,500, about 3,000 to about 5,000, about 3,000 to about 5,500, about 3,000 to about 6,000, about 3,000 to about 7,000, about 3,000 to about 8,000, about 3,000 to about 9,000, about 3,000 to about 10,000, about 4,000 to about 4,500, about 4,000 to about 5,000, about 4,000 to about 5,500, about 4,000 to about 6,000, about 4,000 to about 7,000, about 4,000 to about 8,000, about 4,000 to about 9,000, about 4,000 to about 10,000, about 4,500 to about 5,000, about 4,500 to about 5,500, about 4,500 to about 6,000, about 4,500 to about 7,000, about 4,500 to about 8,000, about 4,500 to about 9,000, about 4,500 to about 10,000, about 5,000 to about 5,500, about 5,000 to about 6,000, about 5,000 to about 7,000, about 5,000 to about 8,000, about 5,000 to about 9,000, about 5,000 to about 10,000, about 5,500 to about 6,000, about 5,500 to about 7,000, about 5,500 to about 8,000, about 5,500 to about 9,000, about 5,500 to about 10,000, about 6,000 to about 7,000, about 6,000 to about 8,000, about 6,000 to about 9,000, about 6,000 to about 10,000, about 7,000 to about 8,000, about 7,000 to about 9,000, about 7,000 to about 10,000, about 8,000 to about 9,000, about 8,000 to about 10,000, or about 9,000 to about 10,000 most variably expressed genes of the initial gene set or the first gene set or both. In some embodiments, N is about 500, about 1,000, about 2,000, about 3,000, about 4,000, about 4,500, about 5,000, about 5,500, about 6,000, about 7,000, about 8,000, about 9,000, or about 10,000. In some embodiments, N is about 500, about 1,000, about 2,000, about 3,000, about 4,000, about 4,500, about 5,000, about 5,500, about 6,000, about 7,000, about 8,000, about 9,000, or about 10,000, most variably expressed genes of the initial gene set or the first gene set or both. In some embodiments, N is at most about 1,000, about 2,000, about 3,000, about 4,000, about 4,500, about 5,000, about 5,500, about 6,000, about 7,000, about 8,000, about 9,000, or about 10,000. In some embodiments, N is at most about 1,000, about 2,000, about 3,000, about 4,000, about 4,500, about 5,000, about 5,500, about 6,000, about 7,000, about 8,000, about 9,000, or about 10,000, most variably expressed genes of the initial gene set or the first gene set or both.

[0155]

[0136] In some embodiments, the plurality of gene modules comprise N genes. In some embodiments, the gene module comprises N genes. In some embodiments, the plurality of gene modules comprise N genes, grouped in consideration of the coexpression of the N genes. In some embodiments, the coexpression of the N genes in a biological sample is assayed. In some embodiments, the coexpression of the N genes in a plurality of biological samples is assayed. In some emboidments, the co-expressed genes are grouped within a gene module. In some embodiments, genes with similar gene expression profiles are grouped within a gene module. In some embodiments, genes having similar expression profiles in the plurality of reference biological samples are grouped within a gene module. In some embodiments, coexpression of the N genes in the plurality of reference biological samples is analyzed using a gene coexpression network analysis. In some embodiments, the N genes are grouped into the plurality of gene modules based on the gene coexpression network analysis. In some embodiments, the gene coexpression network analysis is performed using multiscale embedded gene coexpression network analysis (MEGENA), and / or weighted gene coexpression network analysis (WGCNA). In some embodiments, the N genes are grouped into the plurality of gene modules by developing a planar filtered network (PFN) graph based on gene pair coexpression of the N genes in the plurality of reference biological samples. In some embodiments, the N genes are grouped into the plurality of gene modules by developing a PFN graph based on gene pair coexpression of the N genes in the plurality of reference biological samples, and extracting multiscale modules existing within the PFN graph to form the plurality of gene modules. In some embodiments, the N genes are compared to each other to identify coexpression similarities. In some embodiments, two N genes coexpression with similar expression are paired as co-expressed genes. In some embodiments, gene comparisons to identify paiwise coexpression similaritiesare assigned a global false discovery rate (FDR) calculation. In some embodiments, gene pairs below a FDR p threshold are removed. In some embodiments, the FDR p threshold is < 0.35, < 0.3, < 0.25, < 0.2, < 0.1, < 0.05, or < 0.01. In some embodiments, the FDR p threshold is < 0.2. In some embodiments, the removal of gene pairs reduces the risk of a random choice affecting the strength of the correlation.

[0156]

[0137] In some embodiments, the N genes are grouped into the plurality of gene modules by developing a planar filtered network (PFN) graph based on gene pair coexpression of the N genes, and extracting multiscale modules existing within the PFN graph to form the plurality of gene modules. In some embodiments, the PFN graph is generated by forming an adjacency matrix based on gene pair coexpression; ordering gene pairs according to strength of interaction and meeting a minimal false discovery rate; mapping gene pairs onto a sphere and add edges between them if and only if the resulting graph can still be embedded on a surface of a given genus g = k, where the edges are prohibited from crossing each other and the network wraps around on itself as the topological triangulations between cliques covering the sphere. In some embodiments, the extracting multiscale modules existing within the PFN graph comprises iteratively extracting multiscale modules from topological cliques, wherein the iteration continues until a threshold a resolution parameter is met, and the module sizes decrease and approach the minimum threshold module size requirement (e.g., a threshold minimum size). In some embodiments, a second pass of statistical stringency can be performed to eliminate modules not meeting desired module requirements including minimal and maximum module size and significant gene module compactness. In some embodiments, multiscale hub analysis (MHA) can be performed to identify module hub genes, defined as those genes with intramodular connections meeting a minimal significant hub degree.

[0138] In some embodiments, the one or more sample traits comprise clinical traits such as disease severity index, disease diagnostic parameter, etc.; of the reference patients. In some embodiments, the one or more sample traits comprise biographical traits such as age, ancestry, gender, etc.; of the reference patients. In some embodiments, the one or more sample traits comprise lifestyle traits such as some drug usage, smoking habits, drinking habits, exercise habits, etc.; of the reference patients. In some embodiments, the one or more sample traits comprisedepend on the disease or disorder. In some embodiments, the one or more sample traits, depend on the endotype. In some embodiments, a sample trait of the one or more sample traits of the reference patient is a subjective sample trait or an objective sample trait. In some embodiments, the subjective sample trait comprises disease level (such as PASI (Psoriasis Area and Severity Index)), areas of pain, ancestry, gender, anecdotal features that are described by the patient or observed by a clinician but not objectively (quantifiably) measurable, or combinations thereof. In some embodiments, clinical considerations, objective laboratory assay results, and / or patient attributes for a subjective sample trait are retained as continuous numerical values, or encoded as discrete binary values (e.g., no=0 or yes=l). In some embodiments, the objective sample traits comprise blood autoimmune antibody level, blood complement component 3 (C3) protein level, age, drug usage, features that have quantifiable value, or combinations thereof.

[0157]

[0139] In some embodiments, the strength of correlation of the gene modules with the sample traits as described herein is measured by a suitable method. In some embodiments, the strength of correlation of the gene modules with the one or more sample traits is measured by a suitable method. In some embodiments, the strength of correlation of the plurality of gene modules with the one or more sample traits is measured by a suitable method. In some embodiments, correlation and strength of correlation of the gene modules of the plurality of gene modules with the one or more sample traits is measured by a suitable method. In some embodiments, the one or more gene modules comprise all the gene modules of the plurality of gene modules. In some embodiments, all the gene modules of the plurality of gene modules are correlated with the one or more sample traits. In some embodiments, the one or more gene modules comprise a third generation gene module. In some embodiments, the one or more gene modules comprise a third generation gene modules of the plurality of gene modules. In some embodiments, the third generation gene modules of the plurality of gene modules are correlated with the one or more sample traits. In some embodiments, the plurality of gene modules are obtained using the gene coexpression network analysis. In some embodiments, the gene module MEs are correlated to one or more sample traits. In some embodiments, the gene module MEs are correlated to one or more sample traits of a reference patient. In some embodiments, the gene module MEs are correlated to one or more sample traits of a plurality of reference patients. In some embodiments, the plurality of reference patients and the gene modules are correlated, and the gene module MEs are correlated to one or more sample traits. In some embodiments, the gene module MEs of a patient are correlated to one or more sample traits of a patient. In some embodiments, wherein gene module MEs of a reference patient are correlated to one or more sample traits of the reference patient. In some embodiments, calculating the MEs of the gene module comprises gene expression data. In some embodiments, calculating the MEs of the gene module comprises a data set as described herein. In some embodiments, a group of genes is considered as a gene module for calculating the MEs of the gene module. In some embodiments, a group of genes with similar gene expression profiles is considered as a gene module for calculating the MEs of the gene module. In some embodiments, a plurality of reference patients is referred to as a cohort. In some embodiments, sample trait correlations for a plurality of reference patients or a cohort are assessed. In some embodiments, sample trait correlation that are not significant based on a threshold p value are set to zero. In some embodiments, for the plurality of reference patients, absolute value of significant correlation to cohort is ranked by row means, and gene modules with desired highest significant absolute value of mean correlations are selected as the plurality of significant gene modules. In some embodiments, for example, 30 gene modules are selected as the plurality of significant gene modules. In some embodiments, for example, the gene modules with the 30 highest significant absolute value of mean correlations are selected as the 30 gene modules. In some embodiments, the correlations are measured based on Pearson’s correlation coefficient. In some embodiments, the threshold p value is 0.3, 0.25, 0.2, 0.1, 0.05, or 0.01. In some embodiments, the threshold p value is 0.2. In some embodiments, correlations with p values of < 0.3, < 0.25, < 0.2, < 0.1, < 0.05, or < 0.01, capture known and validated correlations and biological processes while maintaining statistical integrity and reproducibility.

[0158]

[0140] In some embodiments, the plurality of significant gene modules comprise about 10 to about 80 gene modules. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 80 gene modules, that are most strongly correlated with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 80 gene modules, that are correlated among the plurality of gene modules. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 80 gene modules that are correlated among the gene modules. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 80 gene modules that are most strongly correlated among the plurality of gene modules, with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 20 gene modules, about 10 gene modules to about 25 gene modules, about 10 gene modules to about 30 gene modules, about 10 gene modules to about 35 gene modules, about 10 gene modules to about 40 gene modules, about 10 gene modules to about 45 gene modules, about 10 gene modules to about 50 gene modules, about 10 gene modules to about 55 gene modules, about 10 gene modules to about 60 gene modules, about 10 gene modules to about 70 gene modules, about 10 gene modules to about 80 gene modules, about 20 gene modules to about 25 gene modules, about 20 gene modules to about 30 gene modules, about 20 gene modules to about 35 gene modules, about 20 gene modules to about 40 gene modules, about 20 gene modules to about 45 gene modules, about 20 gene modules to about 50 gene modules, about 20 gene modules to about 55 gene modules, about 20 gene modules to about 60 gene modules, about 20 gene modules to about 70 gene modules, about 20 gene modules to about 80 gene modules, about 25 gene modules to about 30 gene modules, about 25 gene modules to about 35 gene modules, about 25 gene modules to about 40 gene modules, about 25 gene modules to about 45 gene modules, about 25 gene modules to about 50 gene modules, about 25 gene modules to about 55 gene modules, about 25 gene modules to about 60 gene modules, about 25 gene modules to about 70 gene modules, about 25 gene modules to about 80 gene modules, about 30 gene modules to about 35 gene modules, about 30 gene modules to about 40 gene modules, about 30 gene modules to about 45 gene modules, about 30 gene modules to about 50 gene modules, about 30 gene modules to about 55 gene modules, about 30 gene modules to about 60 gene modules, about 30 gene modules to about 70 gene modules, about 30 gene modules to about 80 gene modules, about 35 gene modules to about 40 gene modules, about 35 gene modules to about 45 gene modules, about 35 gene modules to about 50 gene modules, about 35 gene modules to about 55 gene modules, about 35 gene modules to about 60 gene modules, about 35 gene modules to about 70 gene modules, about 35 gene modules to about 80 gene modules, about 40 gene modules to about 45 gene modules, about 40 gene modules to about 50 gene modules, about 40 gene modules to about 55 gene modules, about 40 gene modules to about 60 gene modules, about 40 gene modules to about 70 gene modules, about 40 gene modules to about 80 gene modules, about 45 gene modules to about 50 gene modules, about 45 gene modules to about 55 gene modules, about 45 gene modules to about 60 gene modules, about 45 gene modules to about 70 gene modules, about 45 gene modules to about 80 gene modules, about 50 gene modules to about 55 gene modules, about 50 gene modules to about 60 gene modules, about 50 gene modules to about 70 gene modules, about 50 gene modules to about 80 gene modules, about 55 gene modules to about 60 gene modules, about 55 gene modules to about 70 gene modules, about 55 gene modules to about 80 gene modules, about 60 gene modules to about 70 gene modules, about 60 gene modules to about 80 gene modules, or about 70 gene modules to about 80 gene modules. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 20 gene modules, about 10 gene modules to about 25 gene modules, about 10 gene modules to about 30 gene modules, about 10 gene modules to about 35 gene modules, about 10 gene modules to about 40 gene modules, about 10 gene modules to about 45 gene modules, about 10 gene modules to about 50 gene modules, about 10 gene modules to about 55 gene modules, about 10 gene modules to about 60 gene modules, about 10 gene modules to about 70 gene modules, about 10 gene modules to about 80 gene modules, about 20 gene modules to about 25 gene modules, about 20 gene modules to about 30 gene modules, about 20 gene modules to about 35 gene modules, about 20 gene modules to about 40 gene modules, about 20 gene modules to about 45 gene modules, about 20 gene modules to about 50 gene modules, about 20 gene modules to about 55 gene modules, about 20 gene modules to about 60 gene modules, about 20 gene modules to about 70 gene modules, about 20 gene modules to about 80 gene modules, about 25 gene modules to about 30 gene modules, about 25 gene modules to about 35 gene modules, about 25 gene modules to about 40 gene modules, about 25 gene modules to about 45 gene modules, about 25 gene modules to about 50 gene modules, about 25 gene modules to about 55 gene modules, about 25 gene modules to about 60 gene modules, about 25 gene modules to about 70 gene modules, about 25 gene modules to about 80 gene modules, about 30 gene modules to about 35 gene modules, about 30 gene modules to about 40 gene modules, about 30 gene modules to about 45 gene modules, about 30 gene modules to about 50 gene modules, about 30 gene modules to about 55 gene modules, about 30 gene modules to about 60 gene modules, about 30 gene modules to about 70 gene modules, about 30 gene modules to about 80 gene modules, about 35 gene modules to about 40 gene modules, about 35 gene modules to about 45 gene modules, about 35 gene modules to about 50 gene modules, about 35 gene modules to about 55 gene modules, about 35 gene modules to about 60 gene modules, about 35 gene modules to about 70 gene modules, about 35 gene modules to about 80 gene modules, about 40 gene modules to about 45 gene modules, about 40 gene modules to about 50 gene modules, about 40 gene modules to about 55 gene modules, about 40 gene modules to about 60 gene modules, about 40 gene modules to about 70 gene modules, about 40 gene modules to about 80 gene modules, about 45 gene modules to about 50 gene modules, about 45 gene modules to about 55 gene modules, about 45 gene modules to about 60 gene modules, about 45 gene modules to about 70 gene modules, about 45 gene modules to about 80 gene modules, about 50 gene modules to about 55 gene modules, about 50 gene modules to about 60 gene modules, about 50 gene modules to about 70 gene modules, about 50 gene modules to about 80 gene modules, about 55 gene modules to about 60 gene modules, about 55 gene modules to about 70 gene modules, about 55 gene modules to about 80 gene modules, about 60 gene modules to about 70 gene modules, about 60 gene modules to about 80 gene modules, or about 70 gene modules to about 80 gene modules, that are most strongly correlated, among the plurality of gene modules, with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules to about 20 gene modules, about 10 gene modules to about 25 gene modules, about 10 gene modules to about 30 gene modules, about 10 gene modules to about 35 gene modules, about 10 gene modules to about 40 gene modules, about 10 gene modules to about 45 gene modules, about 10 gene modules to about 50 gene modules, about 10 gene modules to about 55 gene modules, about 10 gene modules to about 60 gene modules, about 10 gene modules to about 70 gene modules, about 10 gene modules to about 80 gene modules, about 20 gene modules to about 25 gene modules, about 20 gene modules to about 30 gene modules, about 20 gene modules to about 35 gene modules, about 20 gene modules to about 40 gene modules, about 20 gene modules to about 45 gene modules, about 20 gene modules to about 50 gene modules, about 20 gene modules to about 55 gene modules, about 20 gene modules to about 60 gene modules, about 20 gene modules to about 70 gene modules, about 20 gene modules to about 80 gene modules, about 25 gene modules to about 30 gene modules, about 25 gene modules to about 35 gene modules, about 25 gene modules to about 40 gene modules, about 25 gene modules to about 45 gene modules, about 25 gene modules to about 50 gene modules, about 25 gene modules to about 55 gene modules, about 25 gene modules to about 60 gene modules, about 25 gene modules to about 70 gene modules, about 25 gene modules to about 80 gene modules, about 30 gene modules to about 35 gene modules, about 30 gene modules to about 40 gene modules, about 30 gene modules to about 45 gene modules, about 30 gene modules to about 50 gene modules, about 30 gene modules to about 55 gene modules, about 30 gene modules to about 60 gene modules, about 30 gene modules to about 70 gene modules, about 30 gene modules to about 80 gene modules, about 35 gene modules to about 40 gene modules, about 35 gene modules to about 45 gene modules, about 35 gene modules to about 50 gene modules, about 35 gene modules to about 55 gene modules, about 35 gene modules to about 60 gene modules, about 35 gene modules to about 70 gene modules, about 35 gene modules to about 80 gene modules, about 40 gene modules to about 45 gene modules, about 40 gene modules to about 50 gene modules, about 40 gene modules to about 55 gene modules, about 40 gene modules to about 60 gene modules, about 40 gene modules to about 70 gene modules, about 40 gene modules to about 80 gene modules, about 45 gene modules to about 50 gene modules, about 45 gene modules to about 55 gene modules, about 45 gene modules to about 60 gene modules, about 45 gene modules to about 70 gene modules, about 45 gene modules to about 80 gene modules, about 50 gene modules to about 55 gene modules, about 50 gene modules to about 60 gene modules, about 50 gene modules to about 70 gene modules, about 50 gene modules to about 80 gene modules, about 55 gene modules to about 60 gene modules, about 55 gene modules to about 70 gene modules, about 55 gene modules to about 80 gene modules, about 60 gene modules to about 70 gene modules, about 60 gene modules to about 80 gene modules, or about 70 gene modules to about 80 gene modules, that are most strongly correlated, among the gene modules, with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules, that are most strongly correlated with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules, that are most strongly correlated, among the gene modules, with the one or more sample. In some embodiments, the plurality of significant gene modules comprise at least about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, or about 70 gene modules. In some embodiments, the plurality of significant gene modules comprise at most about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules. In some embodiments, the plurality of significant gene modules comprise at least about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, or about 70 gene modules, that are most strongly correlated with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise at most about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules, that are most strongly correlated with the one or more sample traits. In some embodiments, the plurality of significant gene modules comprise at least about 10 gene modules, about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, or about 70 gene modules, that are most strongly correlated, among the gene modules, with the one or more sample. In some embodiments, the plurality of significant gene modules comprise at most about 20 gene modules, about 25 gene modules, about 30 gene modules, about 35 gene modules, about 40 gene modules, about 45 gene modules, about 50 gene modules, about 55 gene modules, about 60 gene modules, about 70 gene modules, or about 80 gene modules, that are most strongly correlated, among the gene modules, with the one or more sample. In some embodiments, third generation gene modules are selected. In some embodiments, second, third, and / or fourth generation gene modules are selected. In some embodiments, third generation gene modules are selected, wherein the plurality of significant gene modules comprise 10 to 80 most strongly correlated third generation gene modules. In some embodiments, second, third and / or fourth generation gene modules are selected, wherein the plurality of significant gene modules comprise 10 to 80 most strongly correlated second, third, and / or fourth generation gene modules. In some embodiments, the second, third and / or fourth generation gene modules of the plurality of gene modules are correlated with the one or more sample traits, and the plurality of significant gene modules comprises second, third and / or fourth gene modules. In some embodiments, the second, third and / or fourth gene modules comprise the 20 to 50 gene modules that are most strongly correlated with the one or more sample traits among the second, third and / or fourth generation gene modules of the plurality of gene modules. In some embodiments, the third generation gene modules of the plurality of gene modules are correlated with the one or more sample traits, and the plurality of significant gene modules comprises third generation gene modules. In some embodiments, the third generation gene modules comprise the 20 to 50 gene modules that are most strongly correlated with the one or more sample traits among the third generation gene modules of the plurality of gene modules.

[0159]

[0141] In some embodiments, one or more genes are determined to be redundant in consideration of gene expression data from a biological sample. In some embodiments, one or more genes are determined to be redundant in consideration of gene expression data from a plurality of biological samples. In some embodiments, one or more genes are determined to be redundant in consideration of gene expression data from a plurality of biological samples from reference patients or reference biological samples. In some embodiments, one or more genes are determined to be redundant, and are referred to as redundant genes. In some embodiments, one or more redundant genes are excluded from the methods described herein. In some embodiments, one or more redundant genes are excluded before defining gene sets. In some embodiments, one or more redundant genes are excluded before defining gene modules. In some embodinments, one or more redundant genes are excluded after defining gene modules. In some embodiments, one or more redundant genes are excluded before defining a plurality of gene modules. In some embodinments, one or more redundant genes are excluded after defining a plurality of gene modules. In some embodiments, one or more redundant genes are associated with a correlation coefficient greater than a threshold value as described herein. In some embodiments, the threshold value is 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9 or 0.95.

[0160]

[0142] Provided herein are methods for determining a gene set or gene module capable of classifying a disease or disorder of a patient. In some embodiments, analysis of gene expression data results in the categorization of genes as gene sets or gene modules. In some embodiments, analysis of gene expression data results in the identification of gene modules as described herein. In some embodiments, the gene modules as described herein are associated with a molecular endotype of a disease or disorder. In some embodiments, the plurality of gene modules as described herein are associated with a molecular endotype of a disease or disorder. In some embodiments, the gene modules as described herein are associated with a phenotype of a disease or disorder. In some embodiments, the plurality of gene modules as described herein are associated with a phenotype of a disease or disorder. In some embodiments, the disease or disorder described herein comprises a chronic condition, an inflammatory condition, an autoimmune condition, an psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof. In some embodiments, the disease or disorder described herein comprises a chronic condition, an inflammatory condition, an autoimmune condition, an psoriasis, or combinations thereof. In some embodiments, the disease or disorder is a chronic condition. In some embodiments, the disease or disorder is an inflammatory condition. In some embodiments, the disease or disorder is an psoriasis.

[0161]

[0143] In some embodiments, the disease or disorder is type of psoriasis. In some embodiments, a type of psoriasis is any disease or disorder that a person of skill in the art would consider is a representation of psoriasis. In some embodiments, the disease or disorder is a type of the disease or disorder. In some embodiments, the gene set as described herein is capable of classifying a type of the disease or disorder of a patient.

[0144] In some embodiments, the methods described herein comprise a gene set as described herein. In some embodiments, the gene set is capable of classifying a disease or disorder of a patient. In some embodiments, the gene set is capable of classifying a first disease or disorder of the patient. In some embodiments, the gene set is capable of classifying a second disease or disorder of the patient. In some embodiments, the gene set is capable of classifying a third disease or disorder of the patient. In some embodiments, the gene set is capable of classifying a first disease or disorder, a second disease or disorder, or a third disease or disorder of the patient. In some embodiments, the gene set is capable of classifying whether the patient has a first disease or disorder, a second disease or disorder, or a third disease or disorder. In some embodiments, the gene set is capable of classifying a first disease or disorder, a second disease or disorder, or a third disease or disorder of a plurality of patients. In some embodiments, the gene set is capable of classifying a first disease or disorder, a second disease or disorder, or a third disease or disorder of a plurality of reference patients. In some embodiments, the gene set is capable of classifying the first plurality of patients as having the first disease or disorder. In some embodiments, the gene set is capable of classifying the second plurality of patients as having the second disease or disorder. In some embodiments, the gene set is capable of classifying the third plurality of patients as having the third disease or disorder. In some embodiments, the plurality of patients comprises the first plurality of patients having the first disease or disorder. In some embodiments, the plurality of patients comprises the second plurality of patients having the second disease or disorder. In some embodiments, the plurality of patients comprises the third plurality of patients having the third disease or disorder. In some embodiments, the plurality of patients comprises the first plurality of patients having the first disease or disorder, the second plurality of patients having the second disease or disorder, and the third plurality of patients having the third disease or disorder. In some embodiments, the plurality of patients comprises a first plurality of patients having the first disease or disorder and a second plurality of patients having the second disease or disorder, and the gene set is capable of classifying whether a patient has the first disease or disorder, or the second disease or disorder. In some embodiments, the plurality of patients comprises a plurality of reference patients. In some embodiments, the plurality of patients are classified as having a molecular endotype. In some embodiments, the first disease or disorder is a first molecular endotype. In some embodiments, the second disease or disorder is a second molecular endotype. In some embodiments, the third disease or disorder is a third molecular endotype. In some embodiments, a different disease or disorder corresponds to a different molecular endotype. In some embodiments, the gene set comprising gene expression data is capable of classifying a different molecular endotype. In some embodiments, the gene set comprising gene expression data is capable of classifying a different disease or disorder. In some embodiments, a different disease or disorder corresponds to a different phenotype.

[0162]

[0145] In some embodiments, the method classifies the disease or disorder of the patient 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%. In some embodiments, the method classifies the disease or disorder of the patient 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%. In some embodiments, the method classifies the disease or disorder of the patient with a 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%. In some embodiments, the method classifies the disease or disorder of the patient 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%. In some embodiments, the method classifies the disease or disorder of the patient 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%.

[0163]

[0146] In some embodiments, the method classifies the disease or disorder of the patient with an accuracy of about 85 % to about 100 %. In some embodiments, the method classifies the disease or disorder 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 classifies the disease or disorder 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 classifies the disease or disorder 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 %.

[0164]

[0147] In some embodiments, the method classifies the disease or disorder of the patient with a sensitivity of about 85 % to about 100 %. In some embodiments, the method classifies the disease or disorder 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 classifies the disease or disorder 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 classifies the disease or disorder 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 %.

[0165]

[0148] In some embodiments, the method classifies the disease or disorder of the patient with a specificity of about 85 % to about 100 %. In some embodiments, the method classifies the disease or disorder 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 classifies the disease or disorder 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 classifies the disease or disorder 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 %.

[0166]

[0149] In some embodiments, the method classifies the disease or disorder of the patient with a positive predictive value of about 85 % to about 100 %. In some embodiments, the method classifies the disease or disorder 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 % 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 classifies the disease or disorder 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 classifies the disease or disorder 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 %.

[0150] In some embodiments, the method classifies the disease or disorder of the patient with a negative predictive value of about 85 % to about 100 %. In some embodiments, the method classifies the disease or disorder 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 % 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 classifies the disease or disorder 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 classifies the disease or disorder 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 %.

[0167]

[0151] In some embodiments, the gene modules comprise at least a minimum number of genes to obtain the desired accuracy, sensitivity, specificity, positive predictive value and / or negative predictive value in disease or disorder classification, such disease or disorder classification.

[0168]

[0152] In some embodiments, the machine-learning model comprises the accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, described above, and the accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value of the method is based on the classification parameters of the machine-learning model, as described herein and / or as understood by one of skill in the art.

[0169] III. Methods Directed to a Disease or Disorder of a Patient

[0170]

[0153] In some embodiments, the method described herein comprises identifying a disease or disorder as described herein, classifying a disease or disorder as described herein, identifying a patient’s susceptibility to a disease or disorder as described herein, identifying the progression of the disease or disorder of the patient as described herein, identifying if the patient is likely to respond to a treatment as described herein, identifying if the patient is likely to respond to a treatment comprising administration of a drug as described herein, identifying an effectiveness of the treatment as compared to the progression of the disease or disorder of the patient as described herein, or combinations thereof.

[0171]

[0154] In some embodiments, the method described herein comprises identifying a disease or disorder as described herein. In some embodiments, the method described herein comprises classifying a disease or disorder as described herein. In some embodiments, the method described herein comprises identifying a patient’s susceptibility to a disease or disorder as described herein. In some embodiments, the method described herein comprises identifying the progression of the disease or disorder of the patient as described herein. In some embodiments, the method described herein comprises identifying if the patient is likely to respond to a treatment as described herein. In some embodiments, the method described herein comprises identifying if the patient is likely to respond to a treatment comprising administration of a drug as described herein. In some embodiments, the method described herein comprises identifying an effectiveness of the treatment as compared to the progression of the disease or disorder of the patient as described herein.

[0172]

[0155] In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a first disease or disorder, a second disease or disorder, or a third disease or disorder of the patient. In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a first disease or disorder of the patient. In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a second disease or disorder of the patient. In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a third disease or disorder of the patient.

[0173]

[0156] In some embodiments, the method comprises classifying a first disease or disorder, a second disease or disorder, or a third disease or disorder of the patient. In some embodiments, the method comprises classifying a first disease or disorder of the patient. In some embodiments, the method comprises classifying a second disease or disorder of the patient. In some embodiments, the method comprises classifying a third disease or disorder of the patient.

[0174]

[0157] In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a disease or disorder. In some embodiments, the methods described herein also comprise identifying the progression of the disease or disorder.

[0175]

[0158] In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a chronic condition, an inflammatory condition, an autoimmune condition, a dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof. In some embodiments, the methods described herein also comprise identifying the progression of the disease or disorder, the chronic condition, the inflammatory condition, the autoimmune condition, a dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof.

[0176]

[0159] In some embodiments, the methods described herein comprise identifying, classifying, and / or treating an dermatological disease. In some embodiments, the methods described herein also comprise identifying the progression of the dermatological disease. In some embodiments, the method comprises classifying an dermatological disease of the patient.

[0177]

[0160] In some embodiments, the methods described herein comprise identifying, classifying, and / or treating a psoriasis. In some embodiments, the methods described herein also comprise identifying the progression of the psoriasis.

[0178]

[0161] In some embodiments, the method comprises the administration of a treatment to the patient in consideration of the classification of the disease or disorder of the patient. In some embodiments, the method comprises the classification of the disease or disorder of the patient. In some embodiments, the method comprises the administration of the treatment. In some embodiments, the treatment comprises the administration of a drug. In some embodiments, the treatment comprises administration of a treatment to the patient in consideration of the classification of the disease or disorder of the patient. In some embodiments, the treatment comprises administration of a drug to the patient in consideration of the classification of the disease or disorder of the patient.

[0179]

[0162] In some embodiments, the method comprises identifying an dermatological disease of the patient. In some embodiments, the method comprises the classification of the dermatological disease of the patient. In some embodiments, the method comprises the administration of a treatment to the patient in consideration of the classification of the disease or disorder of the patient as a first disease, a second disease, or a third disease. In some embodiments, the treatment of the patient is directed to the first disease. In some embodiments, the treatment of the patient is directed to the second disease. In some embodiments, the treatment of the patient is directed to the third disease. In some embodiments, the treatment of the patient is directed to an dermatological disease. In some embodiments, the treatment of the patient is directed to a psoriasis. In some embodiments, the treatment of the patient comprises the administration of a drug as described herein.

[0180]

[0163] In some embodiments, the methods described herein comprise a data set. In some embodiments, the data set comprises or is derived from gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280,

[0181] 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375,

[0182] 380, 385, 390, 395, 400, 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000, 1050, 1100,

[0183] 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000 or all genes, selected from the genes in any one of TABLE 4, from the biological sample from the patient. In some embodiments, the data set comprises or is derived from gene expression data of at least 2 genes, selected from the genes in any one of TABLE 4, from the biological sample from the patient.

[0184]

[0164] In some embodiments, the data set comprises or is derived from gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265,

[0185] 270, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360,

[0186] 365, 370, 375, 380, 385, 390, 395, 400, 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000,

[0187] 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000 or all genes, selected from the genes modules in any one of TABLE 4, from the biological sample from the patient. In some embodiments, the data set comprises or is derived from gene expression data of at least 2 genes, selected from the genes modules in any one of TABLE 4, from the biological sample from the patient.

[0188]

[0165] In some embodiments, the genes selected from different Tables as described herein are different. In some embodiments, the genes selected from different Tables as described herein are the same.

[0189]

[0166] In some embodiments, the data set comprises or is derived from gene expression data of effective number of genes selected from the genes in any one of TABLE 4, from the biological sample from the patient, wherein number of genes selected from different Tables may be different or the same. In some embodiments, the data set comprises or is derived from gene expression data of all genes in any one of TABLE 4 In some embodiments, the data set comprises or is derived from gene expression data of all genes in one or more of TABLE 4 In some embodiments, the data set comprises or is derived from gene expression data of all genes in one or more of Tables described herein. In some embodiments, the one or more Tables comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, or 30 Tables. In some embodiments, the one or more Tables comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, or 30 Tables selected from a list comprising TABLE 4. In some embodiments, Tables described herein are selected from a list comprising TABLE 4.

[0190]

[0167] In some embodiments, the method comprises the genes modules in any one of TABLE 4, from the biological sample from the patient.

[0191]

[0168] In some embodiments, the method comprises module eigengenes (MEs). In some embodiments, the method comprises a data set comprises MEs. In some embodiments, the MEs comprise gene expression data. In some embodiments, the MEs comprise gene expression data of genes in any one of TABLE 4 In some embodiments, the MEs comprise gene expression data of genes in one or more of TABLE 4 In some embodiments, the MEs comprise gene expression profiles in a gene module. In some embodiments, the MEs comprise gene expression profiles in a gene module comprising gene coexpression data. In some embodiments, MEs identify the relationships between gene modules. In some embodiments, the method comprises the correlation of the one or more gene modules with one or more sample traits comprises correlating the MEs. In some embodiments, the genes modules in any one of TABLE 4 In some embodiments, the MEs comprise gene expression data of genes modules in any one of TABLE 4 In some embodiments, the MEs comprise gene expression data of gene modules in one or more of TABLE 4 In some embodiments, the data set comprises MEs comprising gene expression data of genes modules in any one of TABLE 4

[0192]

[0169] In some embodiments, the data set is derived from the gene expression data using GSVA, wherein the data set comprises one or more GSVA scores of the patient, wherein the one or more GSVA scores are generated based on the gene expression data of genes or gene modules in one or more of Tables described herein. In some embodiments, the data set is derived from the gene expression data using GSVA, wherein the data set comprises one or more GSVA scores of the patient, wherein the one or more GSVA scores are generated based on the gene expression data of genes or gene modules in any one of TABLE 4. In some embodiments, the data set is derived from the gene expression data using GSVA, wherein the data set comprises one or more GSVA scores of the patient, wherein the one or more GSVA scores are generated based on the gene expression data of genes or gene modules in one or more of TABLE 4. In some embodiments, at least one GSVA score of the patient is generated based on enrichment of gene expression data of the genes selected from a Table described herein. In some embodiments, the one or more GSVA scores comprise each generated GSVA score.

[0193]

[0170] In some embodiments, for each selected Table, the at least one GSVA score of the patient is generated based on enrichment of expression of an effective number of genes selected from the genes listed in the selected Table, in the biological sample.

[0194]

[0171] In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model trained to generate an inference of whether the data set is indicative of the patient having a disease or disorder. In some embodiments, the inference is used in classifying that the patient has a disease or disorder.

[0195]

[0172] In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient based on the inference. In some embodiments, the data set comprises the one or more GSVA scores of the patient, and the machine learning model generate the inference based at least on the one or more GSVA scores. In some embodiments, the data set comprises the MEs, and the machine learning model generate the inference based at least on the MEs. In some embodiments, the method further comprises receiving, as an output of the trained machine learning model, the inference; and / or electronically outputting a report indicating the first disease or disorder disease or disorder of the patient based on the inference. In some embodiments, the report comprises nucleic acid sequencing data, transcriptome data, genome data, epigenetic data, proteome data, metabolome data, virome data, metabolome data, methylome data, lipidomic data, lineage-ome data, nucleosomal occupancy data, a genetic variant, a gene fusion, an indel, or combinations thereof.

[0173] In some embodiments, analyzing the data set comprises generating a disease risk score of the patient based on the data set, and classifying whether the data set is indicative of the patient having the disease or disorder based on the disease risk score. The disease risk score of the patient is generated based on the one or more GSVA scores of the patient. In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient with an accuracy of at least 85%. In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient with a sensitivity of at least 85%. In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient with a specificity of at least 85%. In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient with a positive predictive value of at least 85%. In some embodiments, the methods described herein comprise classifying the disease or disorder of the patient with a negative predictive value of at least 85%.

[0196]

[0174] In some embodiments, the methods described herein comprise classifying a disease or disorder of a patient by analyzing a data set comprising or derived from gene expression data of at least 2 genes selected from the genes listed in as shown in TABLE 4, from a biological sample obtained or derived from the patient, to classify the disease or disorder of the patient. In some embodiments, classifying the disease or disorder of the patient can comprise classifying (e.g., determining) whether the patient has the disease or disorder. In some embodiments, the data set comprises or is derived from gene expression data of at least 2, 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,

[0197] 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115,

[0198] 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210,

[0199] 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 305,

[0200] 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 395, 400,

[0201] 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000 or all genes, selected from the genes listed in as shown in TABLE 4, from the biological sample from the patient.

[0202]

[0175] In some embodiments, the data set comprises or is derived from gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265,

[0203] 270, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360,

[0204] 365, 370, 375, 380, 385, 390, 395, 400, 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000, or any number of the genes listed in TABLE 4, from the biological sample from the patient.

[0205]

[0176] In some embodiments, the data set is derived from the gene expression data using gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene coexpression network analysis (MEGENA), weighted gene coexpression network analysis (WGCNA), differential expression analysis, Z-score, log2 expression analysis, or any combination thereof. In some embodiments, the data set is derived from the gene expression data using GSVA. In some embodiments, the data set is derived from the gene expression data using GSVA, wherein the data set comprises one or more GSVA scores of the patient, wherein the one or more GSVA scores are generated based on genes listed in TABLE 4, wherein for each selected Table, at least one GSVA score of the patient is generated based on enrichment of expression of the genes selected from the selected Table, in the biological sample, and wherein the one or more GSVA scores comprise each generated GSVA score.

[0206]

[0177] In some embodiments, the methods described herein comprise determining different patient populations, different patient treatment groups, and / or different patient subsets. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of at least one gene set variation analysis (GSVA). In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of GSVA scores and k-means clustering method. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of module eigengenes (MEs) of the significant gene clusters as described herein. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of MEs and k-means clustering method. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of a machine learning model as described herein. In some embodiments, the machine learning model is trained as described herein, and / or as understood by one of ordinary skill in the art.

[0207]

[0178] In some embodiments, the methods described herein comprise classifying a disease or disorder of a patient. In some embodiments, the methods comprise classifying a disease or disorder of a patient comprises: analyzing a data set comprising gene expression data. In some embodiments, the methods comprise gene expression data of at least 2 genes associated with a gene module or a plurality of significant gene modules. In some embodiments, the methods comprise gene expression data of at least 2 genes from a sample from the patient to classify the disease or disorder of the patient.

[0208]

[0179] In some embodiments, the methods described herein comprise gene expression data of at least 2 genes selected from the genes listed within a gene set. In some embodiments, the methods described herein comprise gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 395, 400, 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000 or all genes, selected from the genes within a gene set.

[0209]

[0180] In some embodiments, the methods described herein comprise gene expression data of at least 2 genes selected from the genes listed within the gene module. In some embodiments, the methods described herein comprise gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 395, 400, 450, 500, 550, 600, 650, 700, 750, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, 1450, 1500, 1550, 1600, 1700, 1800, 1900, 2000 or all genes, selected from the genes within a gene module.

[0210]

[0181] In some embodiments, the methods described herein comprise gene expression data of at least 2 genes from the genes within a gene module as described herein. In some embodiments, the methods described herein comprise gene expression data of genes within one or more gene modules as described herein. In some embodiments, the one or more gene modules are selected from the significant gene modules. In some embodiments, the methods described herein comprise gene expression data of genes selected from TABLE 4. In some embodiments, the methods described herein comprise gene expression data of genes associated with a gene module selected from TABLE 4. In some embodiments, the methods described herein comprise gene expression data of genes associated with a plurality of gene modules selected from TABLE 4. In some embodiments, the methods described herein comprise gene expression data of an effective number of genes associated with a plurality of significant gene modules selected from TABLE 4.

[0211]

[0182] In some embodiments, the methods described herein comprise classifying a disease or disorder of a patient by analyzing a data set comprising or derived from gene expression data of at least 2 genes selected from the genes modules in any one of TABLE 4. In some embodiments, the method comprises analyzing a data set comprising gene expression data from a biological sample isolated from the patient, to classify the disease or disorder of the patient. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a first disease, a second disease, or a third disease. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a chronic condition, an inflammatory condition, an autoimmune condition, a dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a dermatological condition. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a skin disease. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a dermatitis. In some embodiments, classifying the disease or disorder of the patient comprises the classification of the disease or disorder of the patient as a psoriasis.

[0212]

[0183] In some embodiments, the methods described herein comprise selecting an effective number of genes from a Table / module (e.g., a Table from TABLE 4) comprises selecting at least minimum number of genes from the Table / module to obtain desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value in classification of the disease or disorder of the patient. In some embodiments, desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, is an accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value respectively described herein. In some embodiments, the desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, is at least 85%. In some embodiments, the desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, is at least 90%. In some embodiments, the desired accuracy, sensitivity, specificity, positive predictive value, and / or negative predictive value, is at least 95%. In some embodiments, effective number of genes for a module / Table are determined using adjusted rand index (ARI) method. In some embodiments, effective number of genes for a module / Table are determined by performing k-means clustering on randomly selected groups of genes by standard interval based on the total number of genes of the respective Table / module. In some embodiments, similarity is measured by adjusted rand index (ARI). In some embodiments, the adjusted rand index (ARI) is calculated between K-Means module memberships from each randomly selected group of genes to the module memberships obtained using total number of genes of the respective Table / module. In some embodiments, the higher the ARI, the stronger the module memberships suggesting sufficient genes are selected. In some embodiments, the lower the ARI the weaker the module memberships suggesting more genes are required. In some emboidments, the ARI is calculated to determine the effective number of genes for each Table / module selected. In some embodiments, selecting the effective number of genes from a Table (e.g, a Table from TABLE 4) comprises selecting at least 60%, 70%, 80 %, 90%, or all genes from the Table. In some embodiments, selecting effective number of genes from a Table (e.g., a Table from TABLE 4) comprises selecting at least 60%, 70%, 80 %, 90%, or all genes from the Table, where the Table comprises 100 or more genes. In some embodiments, selecting effective number of genes from a Table (e.g., a Table from TABLE 4) comprises selecting at least 70%, genes from the Table, where the Table comprises 100 or more genes. In some embodiments, selecting effective number of genes from a Table (e.g., a Table from TABLE 4) comprises selecting at least 80 %, 90%, 95% or all genes from the Table, where the Table comprises less than 100 genes. In some embodiments, selecting effective number of genes from a Table (e.g, a Table from TABLE 4) comprises selecting all genes from the Table, where the Table comprises less than 100 genes. In some embodiments, at least a minimum number of genes from the Tables (e.g., from TABLE 4, such as based on the absolute coefficient value of the Tables) are selected, such that the method classifies the disease or disorder of the patient with desired accuracy, sensitivity, specificity, positive predictive value and / or negative predictive value, such as at least 85% accuracy, at least 85% sensitivity, at least 85% specificity, at least 85% positive predictive value and / or at least 85% negative predictive value. In some embodiments, an effective number of genes from each of the selected Tables (e.g., from TABLE 4) are selected, such that the method classifies the disease or disorder of the patient with desired accuracy, sensitivity, specificity, positive predictive value and / or negative predictive value, such as at least 85% accuracy, at least 85% sensitivity, at least 85% specificity, at least 85% positive predictive value and / or at least 85% negative predictive value. In some embodiments, at least a minimum number of genes from the Tables (e.g., from TABLE 4, such as based on the absolute coefficient value of the Tables) and an effective number of genes from each of the selected Tables are selected, such that the method classifies the disease or disorder of the patient with desired accuracy, sensitivity, specificity, positive predictive value and / or negative predictive value, such as at least 85% accuracy, at least 85% sensitivity, at least 85% specificity, at least 85% positive predictive value and / or at least 85% negative predictive value.

[0213] Data Analysis Methods and Machine learning Model

[0214]

[0184] In some embodiments, the methods described herein comprise gene modules as described herein. In some embodiments, the gene modules comprise the significant gene modules of the gene set. In some embodiments, the gene modules comprise the significant gene modules of the gene set. In some embodiments, the methods described herein comprise data analysis and a machine learning model. In some embodiments, the methods described herein comprise more than one data analysis method described herein and the machine learning model described herein. In some embodiments, the methods described herein comprise a GVSA score. In some embodiments, the methods described herein comprise the GVSA score and the machine learning model described herein.

[0215]

[0185] In some embodiments, the methods described herein comprise a data set as described herein. In some embodiments, the data set is derived from the gene expression data using gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene coexpression network analysis (MEGENA), weighted gene coexpression network analysis (WGCNA), differential expression analysis, Z-score, log2 expression analysis, or any combination thereof. In some embodiments, the data set is derived from the gene expression data using GSVA. In some embodiments, the data set comprises one or more GSVA scores.

[0216]

[0186] In some embodiments, the data set as described herein is a patient data set. In some embodiments, the patient data set is derived from the gene expression data using gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), enrichment algorithm, multiscale embedded gene coexpression network analysis (MEGENA), weighted gene coexpression network analysis (WGCNA), differential expression analysis, Z-score, log2 expression analysis, or any combination thereof. In some embodiments, the patient data set is derived from the gene expression data using GSVA. In some embodiments, the patient data set comprises one or more GSVA scores of the patient.

[0217]

[0187] In some embodiments, the one or more GSVA scores as described herein are generated based on one or more gene modules selected from the significant gene modules of the gene set. In some embodiments, a selected module is one of the one or more gene modules selected from the significant gene modules. In some embodiments, a GVSA score is generated based on enrichment of gene expression data as described herein. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from genes within a gene module. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from one or more gene modules. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from one or more significant gene modules. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from a plurality of gene modules.

[0218]

[0188] In some embodiments, a GVSA score is generated based on enrichment of gene expression data of at least 2 genes. In some embodiments, a GVSA score is generated based on enrichment of gene expression data of at least 2 genes associated with the gene module. In some embodiments, a GVSA score is generated based on enrichment of gene expression data of at least 2 genes associated with one or more gene module. In some embodiments, a GVSA score is generated based on enrichment of gene expression data of at least 2 genes associated with the plurality of gene modules.

[0219]

[0189] In some embodiments, a GVSA score is generated based on enrichment of gene expression data from a sample. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from a biological sample. In some embodiments, a GVSA score is generated based on enrichment of gene expression data from an isolated biological sample from the patient.

[0220]

[0190] In some embodiments, at least one GSVA score of the patient is generated based on enrichment of gene expression data of at least 2 genes associated with the selected gene module. In some embodiments, the one or more GVSA scores as described herein also referred to as a generated GSVA score. In some embodiments, the one or more GVSA scores comprise a generated GVSA score.

[0221]

[0191] In some embodiments, at least one GSVA score of the patient is generated based on enrichment of gene expression data of an effective number of genes selected from the genes listed in the selected gene module. In some embodiments, the selected gene module comprises genes present in the biological sample. In some embodiments, the selected gene module comprises genes that are equal from the genes present in the biological sample. In some embodiments, the selected gene module comprises genes that are different from the genes present in the biological sample.

[0192] In some embodiments, the methods described herein comprise analyzing the data set as described herein. In some embodiments, analyzing the data set comprises inputting the data set into a machine learning model. In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model. In some embodiments, the machine learning model is trained to generate an inference indicative of a disease of disorder.

[0222]

[0193] In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model trained to generate an inference of whether the data set is indicative of the patient having a disease or disorder. In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model trained to generate an inference of whether the data set is indicative of a patient having a disease or disorder. In some embodiments, the data set comprises the one or more GSVA scores of the patient, and the machine learning model generate the inference based at least on the one or more GSVA scores.

[0223]

[0194] In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model trained to generate an inference of whether the data set is indicative of the patient having a disease or disorder. In some embodiments, analyzing the data set comprises providing the data set as an input to a machine learning model trained to generate an inference of whether the data set is indicative of the patient having a disease or disorder. In some embodiments, the inference is used in classifying that the patient has a disease or disorder.

[0224]

[0195] In some 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 whether the patient data set is indicative of the patient having a disease or disorder. In some embodiments, the 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 whether the patient data set is indicative of the patient having the disease or disorder. In some embodiments, the patient data set comprises the one or more GSVA scores of the patient, and the machine learning model generate the inference based at least on the one or more GSVA scores.

[0225]

[0196] In some embodiments, the methods described herein comprise receiving, as an output of the machine learning model, the inference; and / or electronically outputting a report indicating the disease or disorder of the patient based on the inference.

[0226]

[0197] In some embodiments, the inference from the machine learning model comprises a confidence value 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. In some embodiments, the higher confidence values are correlated with a higher likelihood. In some embodiments, the inference from the machine learning model comprises a confidence value 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, such that the inference is indicative of a disease or disorder. In some embodiments, the inference from the machine learning model can comprise a confidence value 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, indicating the disease or disorder of a patient. In some embodiments, the inference from the machine learning model can comprise a confidence value 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, indicating the patient having a disease or disorder.

[0227]

[0198] In some embodiments, the machine learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) at least 0.85. In some embodiments, the methdos described herein are directed to a method of training a machine learning model. In some embodiments, the machine learning model is traned as described herein, resulting in a trained machine learning model.

[0228]

[0199] 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.

[0229]

[0200] In some embodiments, the machine learning model is trained using linear regression. In some embodiments, the machine learning model is trained using logistic regression (LOG). In some embodiments, the machine learning model is trained using Ridge regression. In some embodiments, the machine learning model is trained using Lasso regression. In some embodiments, the machine learning model is trained using elastic net (EN) regression. In some embodiments, the machine learning model is trained using support vector machine (SVM). In some embodiments, the machine learning model is trained using gradient boosted machine (GBM). In some embodiments, the machine learning model is trained using k nearest neighbors (kNN). In some embodiments, the machine learning model is trained using generalized linear model (GLM). In some embodiments, the machine learning model is trained using naive Bayes (NB) classifier. In some embodiments, the machine learning model is trained using neural network. In some embodiments, the machine learning model is trained using Random Forest (RF). In some embodiments, the machine learning model is trained using deep learning algorithm, linear discriminant analysis (LDA). In some embodiments, the machine learning model is trained using decision tree learning (DTREE). In some embodiments, the machine learning model is trained using adaptive boosting (ADB). In some embodiments, the machine learning model is trained using Classification and Regression Tree (CART). In some embodiments, the machine learning model is trained using hierarchical clustering.

[0230]

[0201] In some embodiments, the trained machine learning model has an accuracy of 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%, or more than about 99.5 %. In some embodiments, the trained machine learning model has a sensitivity of 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 trained machine learning model has a specificity of 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 trained machine learning model has a positive predictive value of 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 trained machine learning model has a negative predictive value of 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%.

[0231]

[0202] In some embodiments, the trained machine learning model has an accuracy of about 85% to about 100 %. In some embodiments, the trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has an accuracy of at most 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 %.

[0232]

[0203] In some embodiments, the trained machine learning model has a sensitivity of about 85 % to about 100 %. In some embodiments, the trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has a sensitivity of at most 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 %.

[0233]

[0204] In some embodiments, the trained machine learning model has a specificity of about 85% to about 100 %. In some embodiments, the trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has 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 trained machine learning model has a specificity of at most 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 %.

[0234]

[0205] In some embodiments, the trained machine learning model has a positive predictive value of about 85 % to about 100 %. In some embodiments, the trained machine learning model has 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 % 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

[0235] 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 trained machine learning model has 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 trained machine learning model has a positive predictive value of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about

[0236] 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the trained machine learning model has a positive predictive value of at most 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 %.

[0237]

[0206] In some embodiments, the trained machine learning model has a negative predictive value of about 85 % to about 100 %. In some embodiments, the trained machine learning model has 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 % 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

[0238] 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 trained machine learning model has 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 trained machine learning model has a negative predictive value of at least about 85 %, about 90 %, about 92 %, about 94 %, about 95 %, about

[0239] 96 %, about 98 %, about 99 %, about 99.3 %, about 99.5 %, or about 99.8 %. In some embodiments, the trained machine learning model has a negative predictive value of at most 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 %.

[0240]

[0207] In some embodiments, the trained machine learning model has a receiver operating characteristic (ROC) curve with an Area-Under-Curve (AUC) 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. In some embodiments, the trained machine learning model has a ROC curve with an AUC of about 0.85 to about 1. In some embodiments, the trained 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 trained 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 trained 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 trained machine learning model has a ROC curve with an AUC of at most 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.

[0241]

[0208] For example, in some embodiments, patient data comprises ranges of patient data (e.g., gene expression data and / or sample trait data) are expressed as a plurality of disjoint continuous ranges of continuous measurement values, and categories of patient data (e.g., gene expression data and / or sample trait data) may be expressed as a plurality of disjoint sets of measurement values (e.g., {“high”, “low”}, {“high”, “normal”}, {“low”, “normal”}, {“high”, “borderline high”, “normal”, “low”}, {“Yes”, “No”}, {“Present”, “Absent”} etc.). In some embodiments, sample traits comprise clinical labels indicating the patient’s health history, such as a diagnosis of a disease or disorder, a previous administering of a clinical treatment (e.g., a drug, a surgical treatment, chemotherapy, radiotherapy, immunotherapy, etc.), physical traits (age, sex, ancestry, etc.), behavioral factors, or other health status (e.g., hypertension or high blood pressure, hyperglycemia or high blood glucose, hypercholesterolemia or high blood cholesterol, history of allergic reaction or other adverse reaction, etc.).

[0242]

[0209] Provided herein are computer systems programmed to implement the methods described herein. In some embodiments, a computer system is programmed or otherwise configured to implement methods provided herein.

[0243]

[0210] In some embodiments, the computer system is an electronic device of a user or a computer system that is remotely located with respect to the electronic device. In some embodiments, the electronic device is a mobile electronic device.

[0244]

[0211] In some embodiments, the computer system comprises a central processing unit (CPU, also “processor” and “computer processor” herein), which is a single core or multi core processor, or a plurality of processors for parallel processing. In some embodiments, the computer system also comprises memory or memory location (e.g., random-access memory, read-only memory, flash memory), electronic storage unit (e.g., hard disk), communication interface (e.g., network adapter) for communicating with one or more other systems, and peripheral devices, such as cache, other memory, data storage and / or electronic display adapters. In some embodiments, the memory, storage unit, interface and peripheral devices are in communication with the CPU through a communication bus (solid lines), such as a motherboard. In some embodiments, the storage unit is a data storage unit (or data repository) for storing data. In some embodiments, the computer system is operatively coupled to a computer network (“network”) with the aid of the communication interface. In some embodiments, the network is the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet.

[0245]

[0212] In some embodiments, the network is a telecommunication and / or data network. In some embodiments, the network comprises one or more computer servers, which enable distributed computing, such as cloud computing. For example, in some embodiments, the one or more computer servers enable cloud computing over the network (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, obtaining a data set comprising gene expression data of genes of an initial gene set, from a plurality of patients; selecting N genes from the initial gene set, said N genes are N variably expressed genes of a first gene set, wherein the first gene set is a subset of the initial gene set, each gene of the first gene set can be mapped to at least one known protein, and N is an integer number; grouping the N genes into a plurality of gene modules based at least on coexpression of the N genes; correlating the plurality of gene modules with one or more sample traits, and selecting a plurality of significant gene modules based at least on strength of the correlation; overlapping one or more significant gene modules with one or more gene function signature lists; annotating the one or more significant gene modules with one or more functional characterizations based on sufficient overlap between one or more significant gene modules and the one or more gene function signature lists, wherein significant overlap satisfies overlap of a threshold minimum number of genes; and partitioning the plurality of patients into two or more treatment groups, wherein (i) all patients in a treatment group are associated with a set of significant gene modules, or (ii) each significant module of the set of significant gene modules is associated with the same functional characterization, or both. In some embodiments, the cloud computing is provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. In some embodiments, the network implements a peer-to-peer network, which enables devices coupled to the computer system to behave as a server. In some embodiments, the network and the computer system implements a peer-to-peer network, which enables devices coupled to the computer system to behave as a server.

[0246]

[0213] In some embodiments, the CPU executes a sequence of machine readable instructions, which can be embodied in a program or software. In some embodiments, the instructions are stored in a memory location, such as the memory . In some embodiments, the instructions are directed to the CPU , which can subsequently program or otherwise configure the CPU to implement methods of the present disclosure. In some embodiments, examples of operations performed by the CPU comprise fetch, decode, execute, and writeback.

[0247]

[0214] In some embodiments, the CPU is part of a circuit, such as an integrated circuit. In some embodiments, the circuit comprises one or more other components of the system . In some embodiments, the circuit is an application specific integrated circuit (ASIC).

[0248]

[0215] In some embodiments, the storage unit stores files, such as drivers, libraries and saved programs. The storage unit can store user data, e.g., user preferences and user programs. In some embodiments, the computer system comprise one or more additional data storage units that are external to the computer system , such as located on a remote server that is in communication with the computer system through an intranet or the Internet.

[0249]

[0216] In some embodiments, the computer system communicates with one or more remote computer systems through the network . In some embodiments, the computer system communicates with a remote computer system of a user. In some embodiments, the remote computer systems comprise personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android- enabled device, Blackberry®), or personal digital assistants. In some embodiments, the user access the computer system via the network .

[0250]

[0217] In some embodiments, methods as described herein are implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system , such as, for example, on the memory or electronic storage unit . In some embodiments, the machine executable or machine readable code is provided in the form of software. In some embodiments, the code is executed by the processor . In some embodiments, the code is retrieved from the storage unit and stored on the memory for ready access by the processor . In some embodiments, the electronic storage unit is precluded, and machine executable instructions are stored on memory .

[0251]

[0218] In some embodiments, the code is pre-compiled and configured for use with a machine having a processor adapted to execute the code, or is compiled during runtime. In some embodiments, the code is supplied in a programming language that is selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0252]

[0219] In some embodiments, aspects of the systems and methods provided herein, such as the computer system , are embodied in programming. In some embodiments, various aspects of the technology are thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. In some embodiments, machine executable code is stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. In some embodiments, machine executable code comprises a computer program including instructions executable by a processor to run an application. In some embodiments, a computer comprises storage media as described herein. In some embodiments, storage media ccomprises any or all of the tangible memory of a computer, processor, or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like. In some embodiments, the storage media as described herein, provides non- transitory storage at any time for a computer program or a software program. In some embodiments, a computer as described herein comprises a computer-readable storage media or a non-transitory computer-readable storage media. In some embodiments, a non-transitory computer-readable storage media comprises non-volatile memory or non-volatile computer memory. In some embodiments, a non-transitory computer-readable storage media econdes a computer program including instructions executable by a processor to run an application for identifying and comparing gene expression data. In some embodiments, a non-transitory computer-readable storage media econdes a software program including instructions executable by a processor to run an application. In some embodiments, a non-transitory computer-readable storage media econdes a software program including instructions executable by a computer to run an application for identifying and / or comparing gene expression data. In some embodiments, all the software or portions of the software are, at times, communicated through the Internet or various other telecommunication networks. In some embodiments, communications through the Internet or various other telecommunication networks enable loading of the software from one computer or processor into another. In some embodiments, loading of the software as described herein comprises, for example, loading from a management server or host computer into the computer platform of an application server. In some embodiments, another type of media that bears the software elements comprises optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible storage media, terms such as computer or machine readable medium refer to any medium that participates in providing instructions to a processor for execution. In some emnodiments, a computer as described herein comprises a program including instructions executable by a processor to run an application.

[0253]

[0220] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media comprise, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media comprise dynamic memory, such as main memory of such a computer platform. Tangible transmission media comprise coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. In some embodiments, non-volative storage media is non-transitory storage media. In some embodiments, non-volative storage media is non- transitory computer-readable storage media. In some embodiments, volative storage media is transitory storage media. In some embodiments, volative storage media is transitory computer- readable storage media. In some embodiments a computer as described herein comprises non- volative storage media and volatile storage media. In some embodiments, a computer as described herein comprises non-transitory storage media and transitory storage media. In some embodiments, a computer as described herein comprises non-transitory storage media encoded with a computer program or a software comprising instructions executable by a processor to run an application. As described herein, storage media is computer-readable media. Common forms of computer-readable media therefore comprise for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0254]

[0221] In some embodiments, the computer system comprises or is in communication with an electronic display that comprises a user interface (UI) . Examples of user interfaces (UIs) comprise, without limitation, a graphical user interface (GUI) and web-based user interface. For example, the computer system can comprise a graphical user interface (GUI) configured to display, for example, patient data, identification of a lung nodule of the patient as a malignant lung nodule or a benign lung nodule, and / or predictions or assessments generated from patient data data.

[0255]

[0222] In some embodiments, the methods and systems provided herein are implemented by way of one or more algorithms. In some embodiments, the algorithm is implemented by way of software upon execution by the central processing unit . In some embodiments, the algorithm, for example, obtains or asseses a data set comprising gene expression data of genes of an initial gene set, from a plurality of patients; select N genes from the initial gene set, said N genes are N variably expressed genes of a first gene set, wherein the first gene set is a subset of the initial gene set, each gene of the first gene set can be mapped to at least one known protein, and N is an integer number; group the N genes into a plurality of gene modules based at least on coexpression of the N genes; correlate the plurality of gene modules with one or more sample traits, and selecting a plurality of significant gene modules based at least on strength of the correlation; overlap one or more significant gene modules with one or more gene function signature lists; annotate the one or more significant gene modules with one or more functional characterizations based on sufficient overlap between one or more significant gene modules and the one or more gene function signature lists, wherein significant overlap satisfies overlap of a threshold minimum number of genes; and partition the plurality of patients into two or more treatment groups, wherein (i) all patients in a treatment group are associated with a set of significant gene modules, or (ii) each significant module of the set of significant gene modules is associated with the same functional characterization, or both.

[0256] IV. Methods of Comprising Different Patient Populations

[0257]

[0223] In some embodiments, the methods described herein comprise different patient populations, different patient treatment groups, and / or different patient subsets. In some embodiments, the methods described herein comprise a patient population comprising subsets of patients. In some embodiments, a patient subset is a patient cluster as described herein.

[0258]

[0224] In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of gene expression data corresponding to a disease or disorder as described herein. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of a gene module corresponding to a disease or disorder as described herein. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of a progression of a disease or disorder. In some embodiments, analysis of gene expression data from a patient sample determines the patient population, treatment group, and / or patient subset for a patient with a disease or disorder as described herein. In some embodiments, analysis of gene expression data from a patient sample as compared to reference gene modules and sample traits determines the patient population, treatment group, and / or patient subset for a patient with a disease or disorder as described herein.

[0259]

[0225] In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of at least one gene set variation analysis (GSVA). In some embodiments, two or more patient populations, two or more patient treatment groups, and / or two or more patient subsets are determined in consideration of at least one gene set variation analysis (GSVA). In some embodiments, GSVA scores are generated using the gene modules as input for GSVA. In some embodiments, GSVA scores are generated using the significant gene modules as input for GSVA. In some embodiments, GSVA scores are generated using the plurality of significant gene modules as input for GSVA. In some embodiments, GSVA scores are generated as described herein, and / or as understood by one of ordinary skill in the art. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of GSVA scores and k-means clustering method. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of module eigengenes (MEs) of the significant gene modules as described herein. In some embodiments, MEs are calculated using the gene modules as input. In some embodiments, MEs are calculated using the significant gene modules as input. In some embodiments, MEs are calculated using the plurality of gene modules as input. In some embodiments, MEs are calculated as described herein, and / or as understood by one of ordinary skill in the art. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of MEs and k-means clustering method.

[0260]

[0226] In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of a machine learning model as described herein. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of a trained machine learning model as described herein. In some embodiments, the machine learning model is trained using the gene expression data as input. In some embodiments, the machine learning model is trained using the gene modules as input. In some embodiments, the machine learning model is trained using the significant gene modules as input. In some embodiments, the machine learning model is trained using the plurality of gene modules as input. In some embodiments, the machine learning model is trained as described herein, and / or as understood by one of ordinary skill in the art.

[0261]

[0227] In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined to be a first patient population, a second patient population, a third patient population, etc.

[0262]

[0228] In some embodiments, the methods described herein comprise methods of treatment of a disease or disorder as described herein. In some embodiments, the methods of treatment of a disease or disorder comprise treatment of patients in a first patient population, and treatment of patients in a second patient population. In some embodiments, the treatment of patients in a first patient population is different from the treatment of patients in a second patient population. In some embodiments, the methods of treatment of a disease or disorder comprise treatment of a first patient in a first patient population, and a second patient in a first patient population. In some embodiments, the treatment of a first patient in a first patient population is different from the treatment of a second patient in a first patient population. In some embodiments, the molecular endotype of a first patient in a first patient population is different from the molecular endotype of a second patient in a first patient population.

[0229] In some embodiments, the methods of treatment of a disease or disorder as described herein comprise treatment of different treatment groups. In some embodiments, the methods of treatment of a disease or disorder as described herein comprise treatment of more than one treatment group. In some embodiments, a treatment group is also referred to as a patient population.

[0263]

[0230] In some embodiments, a gene signature described herein is associated to the genes referred to in TABLE 4.

[0264]

[0231] In some embodiments, the methods described herein comprise a patient population comprising at least two subsets of patients. In some embodiments, a patient subset is a patient cluster as described herein. In some embodiments, the methods described herein comprise a first patient population comprising at least two subsets of patients. In some embodiments, the methods described herein comprise a second patient population comprising at least two subsets of patients. In some embodiments, the methods described herein comprise a third patient population comprising at least two subsets of patients.

[0265]

[0232] In some embodiments, a first patient population comprises different patient subsets, and / or different patient clusters. In some embodiments, a first patient population comprises different patient treatment groups. In some embodiments, a patient subset corresponds to a patient treatment group. In some embodiments, a patient cluster corresponds to a patient treatment group.

[0266]

[0233] In some embodiments, a patient subset, as detailed in FIG. 1-FIG. 20, corresponds to a disease phenotype of a disease or disorder described herein. In some embodiments, a patient cluster, as detailed in FIG. 1-FIG. 20, corresponds to disease phenotype of a disease or disorder described herein. In some embodiments, a patient subset, as detailed in FIG. 1-FIG. 20, corresponds to a patient treatment group. In some embodiments, a patient cluster, as detailed in FIG. 1-FIG. 20, corresponds to a patient treatment group.

[0267]

[0234] In some embodiments, each of the at least two subsets of patients corresponding to a different disease phenotype of a disease or disorder described herein. In some embodiments, each of the at least two subsets of patients corresponding to a different disease phenotype of an established disease or disorder.

[0268]

[0235] In some embodiments, patient subsets or patient clusters are determined using idealized k- means clustering as described herein. In some embodiments, a patient subset or a patient cluster determined using idealized k-means clustering corresponds to a disease phenotype. In some embodiments, a patient subset or a patient cluster determined using idealized k-means clustering corresponds to treatment group. In some embodiments, a patient subset or a patient cluster determined using idealized k-means clustering corresponds to a clinical outcome. In some embodiments, a patient subset or a patient cluster, as detailed in FIG. 1-FIG. 20, is determined using idealized k-means clustering. In some embodiments, stable k-means clustering revealed groupings of clinical traits and correlated molecular functions. In some embodiments, patient subsets or patient clusters are determined using using a machine learning model described herein.

[0269]

[0236] In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of gene expression data from a patient. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of gene expression data from a plurality of patients. In some embodiments, different patient populations, different patient treatment groups, and / or different patient subsets are determined in consideration of gene expression data from at least 2 genes of a plurality of significant gene modules associated with a disease or disorder.

[0270]

[0237] In some embodiments, the first patient population comprises a first patient, a second patient, a third patient, etc. In some embodiements, a first patient is determined to have a different molecular endotype from a second patient. In some embodiments, a first patient is determined to have a different molecular endotype from a third patient. In some embodiments, a second patient is determined to have a different molecular endotype from a third patient. In some embodiments, a first patient population comprises one or more molecular endotypes and / or one or more disease phenotypes, corresponding to one or more treatment groups. In some embodiments, the first patient population comprises one or more patient subsets. In some embodiments, the first patient population comprises patient subsets in consideration of the gene expression data as it relates to molecular endotype, disease phenotype, and treatment group. In some embodiments, the first patient population comprises one or more patient clusters. In some embodiments, the first patient population comprises patient clusters in consideration of the gene expression data as it relates to molecular endotype, disease phenotype, and treatment group.

[0271]

[0238] In some embodiments, the first patient population to the gene expression data of the second patient in the first patient population is used to identify genetic biomarkers that correlate to a clinical profile of a patient or a clinical outcome of a disease or disorder of a patient. In some embodiments, the method for predicting a clinical outcome of a disease or disorder of a patient comprises more than two patients in a first patient population, more than two isolated biological samples, and / or more than two different disease phenotypes.

[0272]

[0239] In some embodiments, a patient population associated with a disease or disorder as described herein is partitioned into the two or more treatment groups based at least on training a machine learning model to infer a treatment group for a reference patient. In some embodiments, the machine learning model is trained to infer a treatment group for a reference patient based on i) gene expressions of at least 2 genes of the plurality of significant gene modules, in a reference biological sample from the reference patient, and / or ii) the reference patient’s one or more sample traits. In some embodiments, the machine learning model is trained to infer a treatment group for a reference patient based on GSVA scores of the reference patient. In some embodiments, the machine learning model is trained to infer a treatment group for a reference patient based on MEs of the reference patient. The GSVA scores and / or MEs of a reference patient can be calculated as described herein. In some embodiments, the machine learning model is trained to infer a treatment group for a reference patient based on i) gene expressions of at least 2 genes of the plurality of significant gene modules, in a reference biological sample from the reference patient, and ii) the reference patient’s one or more sample traits.

[0273]

[0240] In some embodiments, a patient population associated with a disease or disorder as described herein is partitioned into the two or more treatment groups based at least on a machine learning model trained to infer a treatment group for a patient based on the patient’s one or more sample traits. In some embodiments, the machine learning model infers a treatment group for a patient based on i) gene expression data of at least 2 genes of the plurality of significant gene modules in an isolated biological sample from the patient. In some embodiments, the patient population is partitioned into the two or more treatment groups based on gene expression data of at least 2, 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, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270,

[0274] 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365,

[0275] 370, 375, 380, 385, 390, 395, 400, 405, 410, 415, 420, 425, 430, 435, 440, 445, 450, 455, 460,

[0276] 465, 470, 475, 480, 485, 490, 495, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100,

[0277] 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, or 2000 genes of the plurality of significant gene modules in an isolated biological sample from the patient. In some embodiments, the patient population is partitioned into the two or more treatment groups based on gene expression data and / or at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 sample traits.

[0278]

[0241] In some embodiments, the methods described herein comprise determining treatment methods for the two or more treatment groups. In some embodiments, a treatment method for a treatment group is determined in consideration of at least the functional annotation of the one or more significant gene modules associated with the treatment group. In some embodiments, the methods described herein comprise determining treatment methods for the two or more treatment groups in consideration of the progression of the disease or disorder.

[0279]

[0242] In some embodiments, the progression of the disease or disorder determines the methods for treating a disease or disorder in a patient. In some embodiments, the progression of the disease or disorder determines the methods for treating, preventing, or inhibiting a disease or disorder as described herein. In some embodiments, the progression of the disease or disorder shifts clinical outcomes.

[0280] V. Methods for Predicting a Clinical Outcome of a Disease or disorder of a Patient

[0281]

[0243] Described herein are methods for predicting a clinical outcome of a disease or disorder in a patient.

[0282]

[0244] Also provided herein are methods described herein comprising assays for the generation of gene expression data from the sample from the patient, for accurate, repeatable, real-time, determinations of a disease or disorder in a patient (e.g., classifying a disease or disorder, determining progression of disease or disorder, predicting a clinical outcome of a disease or disorder), useful in developing targeted personalized treatment for patients with chronic, inflammatory, and / or autoimmune diseases as described herein.

[0283]

[0245] In some embodiments, methods for predicting a clinical outcome of a disease or disorder in a patient comprise at least two subsets of patients, each of the at least two subsets of patients corresponding to a different disease phenotype of an established disease or disorder.

[0284]

[0246] In some embodiment, the patient is at elevated risk of having disease or disorder. In some embodiment, the patient is suspected of having disease or disorder. In some embodiment, the patient is asymptomatic for disease or disorder. In some embodiment, the patient has disease or disorder. In some embodiment, the patient is at elevated risk of having of having inactive disease or disorder. In some embodiment, the patient is suspected of having inactive disease or disorder. In some embodiment, the patient is asymptomatic for inactive disease or disorder. In some embodiment, the patient has inactive disease or disorder. In some embodiment, the patient is at elevated risk of having of having active disease or disorder. In some embodiment, the patient is suspected of having active disease or disorder. In some embodiment, the patient is asymptomatic for active disease or disorder. In some embodiment, the patient has active disease or disorder. In some embodiments, the disease or disorder as described herein is any type of disease or disorder as described herein.

[0247] In some embodiment, the patient is at elevated risk of having more than one disease or disorder. In some embodiment, the patient is suspected of having more than one disease or disorder. In some embodiment, the patient is asymptomatic for more than one disease or disorder. In some embodiment, the patient has more than one disease or disorder. In some embodiment, the patient is at elevated risk of having of having inactive more than one disease or disorder. In some embodiment, the patient is suspected of having inactive more than one disease or disorder. In some embodiment, the patient is asymptomatic for inactive more than one disease or disorder. In some embodiment, the patient has inactive more than one disease or disorder. In some embodiment, the patient is at elevated risk of having of having active more than one disease or disorder. In some embodiment, the patient is suspected of having active more than one disease or disorder. In some embodiment, the patient is asymptomatic for active more than one disease or disorder. In some embodiment, the patient has active more than one disease or disorder. In some embodiments, the more than one disease or disorder as described herein is any type of more than one disease or disorder as described herein.

[0285]

[0248] In some embodiment, the patient is at elevated risk of having a dermatological condition. In some embodiment, the patient is suspected of having a dermatological condition. In some embodiment, the patient is asymptomatic for a dermatological condition. In some embodiment, the patient has a dermatological condition. In some embodiment, the patient is at elevated risk of having of having an inactive dermatological condition. In some embodiment, the patient is suspected of having an inactive dermatological condition. In some embodiment, the patient is asymptomatic for an inactive dermatological condition. In some embodiment, the patient has an inactive dermatological condition. In some embodiment, the patient is at elevated risk of having an active dermatological condition. In some embodiment, the patient is suspected of having an active dermatological condition. In some embodiment, the patient is asymptomatic for an active dermatological condition. In some embodiment, the patient has an active dermatological condition. In some embodiments, the disease or disorder as described herein is any type of a dermatological condition as described herein. In some embodiment, the patient is at elevated risk of having more than one a dermatological condition. In some embodiment, the patient is suspected of having more than one a dermatological condition. In some embodiment, the patient is asymptomatic for more than one a dermatological condition. In some embodiment, the patient has more than one a dermatological condition. In some embodiments, the patient has and / or experiencing more than one a dermatological condition.

[0286]

[0249] In some embodiment, the patient is at elevated risk of having psoriasis. In some embodiment, the patient is suspected of having psoriasis. In some embodiment, the patient is asymptomatic for psoriasis. In some embodiment, the patient has psoriasis. In some embodiment, the patient is at elevated risk of having of having inactive psoriasis. In some embodiment, the patient is suspected of having inactive psoriasis. In some embodiment, the patient is asymptomatic for inactive psoriasis. In some embodiment, the patient has inactive psoriasis. In some embodiment, the patient is at elevated risk of having of having active psoriasis. In some embodiment, the patient is suspected of having active psoriasis. In some embodiment, the patient is asymptomatic for active psoriasis. In some embodiment, the patient has active psoriasis. In some embodiments, the disease or disorder as described herein is any type of psoriasis as described herein. In some embodiments, the disease or disorder is selected from: a chronic condition, an inflammatory condition, an autoimmune condition, dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, or a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, combinations thereof. In some embodiment, the patient is at elevated risk of having more than one psoriasis. In some embodiment, the patient is suspected of having more than one psoriasis. In some embodiment, the patient is asymptomatic for more than one psoriasis. In some embodiment, the patient has more than one psoriasis. In some embodiments, the patient has and / or experiencing more than one psoriasis.

[0287]

[0250] In some embodiment, the patient is at elevated risk of having dermatitis. In some embodiment, the patient is suspected of having dermatitis. In some embodiment, the patient is asymptomatic for dermatitis. In some embodiment, the patient has dermatitis. In some embodiment, the patient is at elevated risk of having of having inactive dermatitis. In some embodiment, the patient is suspected of having inactive dermatitis. In some embodiment, the patient is asymptomatic for inactive dermatitis. In some embodiment, the patient has inactive dermatitis. In some embodiment, the patient is at elevated risk of having of having active dermatitis. In some embodiment, the patient is suspected of having active dermatitis. In some embodiment, the patient is asymptomatic for active dermatitis. In some embodiment, the patient has active dermatitis. In some embodiments, the disease or disorder as described herein is any type of dermatitis as described herein. In some embodiments, the dermatitis is selected from: an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, combinations thereof. In some embodiment, the patient is at elevated risk of having a dermatitis. In some embodiment, the patient is at elevated risk of having more than one dermatological condition. In some embodiment, the patient is at elevated risk of having more than one dermatitis. In some embodiment, the patient is suspected of having more than one dermatitis. In some embodiment, the patient is asymptomatic for more than one dermatitis. In some embodiment, the patient has more than one dermatitis. In some embodiments, the patient has and / or experiencing more than one dermatitis.

[0288] VI. Methods of Treating a Disease or Disorder

[0289]

[0251] Described herein are methods for treating a disease or disorder in a patient.

[0290]

[0252] In some embodiments, methods comprise treating, preventing, or inhibiting a disease or disorder as described herein. In some embodiments, methods comprise treating, preventing, or inhibiting a disease or disorder associated with a gene module. In some embodiments, methods comprise treating, preventing, or inhibiting a disease or disorder associated with one or more gene modules. In some embodiments, methods comprise treating, preventing, or inhibiting a disease or disorder associated with a plurality of gene modules. In some embodiments, methods for treating a disease or disorder comprise methods of identifying a disease or disorder as described herein. In some embodiments, methods for treating a disease or disorder comprise methods of identifying a disease or disorder or a susceptibility thereof of the patient as described herein. In some embodiments, compositions, systems, or kits described herein are for use in a method for treating a disease or disorder. In some embodiments, compositions, systems, or kits described herein are for use in the manufacture of a medicament for treating a disease or disorder. In some embodiments, compositions, systems, or kits described herein are for the administration of a drug for treating a disease or disorder as described herein. In some embodiments, compositions, systems, or kits described herein are for the administration of a composition for the treatment of a disease or disorder as described herein. In some embodiments, the composition is a pharmaceutical composition.

[0291]

[0253] In some embodiments, the method for treating a disease or disorder comprises identifying the treatment the patient with a disease or disorder is likely to respond to. In some embodiments, the method for treating a disease or disorder comprises the identification of treatment effectiveness and, if necessary, treatment adjustment. In some embodiments, the method for treating a disease or disorder comprises identifying the effectiveness of the treatment as compared to a progression of a disease or disorder in a patient. In some embodiments, the method for treating a disease or disorder comprises the administration of a drug as described herein.

[0292]

[0254] In some embodiments, the method for treating a disease or disorder comprises treatment adjustment in consideration of a method for identifying a report as described herein. In some embodiments, methods described herein comprise electronically outputting a report detailing the comparison of (i) the gene expression data set generated from assaying the isolated biological sample to (ii) the reference gene expression data set from one or more gene modules. In some embodiments, the report is output of a computer comprising a non-transitory computer-readabale storage media encoded with a computer program including instructions executable by a processor to run an application. In some embodiments, the report (i) identifies an immunological state of the patient at an accuracy of at least about 70%; (ii) identifies a disease or disorder or a susceptibility thereof of the patient at an accuracy of at least about 70%; (iii) identifies if the patient is likely to respond to a treatment comprising administration of a drug selected from: an immunoregulator, an immunosuppressant, a steroid, an anti-inflammatory, a JAK inhibitors, a TNF inhibitors, an etanercept, an ustekinumab, a brodalumab, a dupilumab, a brepocitinib, a guselkumab, a secukinumab, a baricitinib, a corticosteroid, a nonsteroidal anti-inflammatory drug (NSAID), a tofacitinib, a upadacitinib, a deucravacitinib, a brepocitinib, a disease-modifying anti-rheumatic drug (DMARD), a conventional synthetic DMARD (csDMARD), a targeted synthetic DMARD (tsDMARD), a biologic DMARD (bDMARD), a biologic treatment, a TYK2 inhibitor, a TYK2 / JAK inhibitor, a combination inhibitor, a monoclonal antibody, an anti-TNF biologic, anti-IL-4 biologic, anti-IL-6 biologic, anti-IL-17 biologic, anti-IL- 12 / 23 biologic, and anti-CD28 biologic, or combinations thereof; and / or (v) identifies an effectiveness of the treatment of the patient as compared to the progression of a disease or disorder.

[0293]

[0255] In some embodiments, the report identifies an immunological state of the patient 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 85%, at least about 90%, at least about 95%, or at least about 100%.

[0294]

[0256] In some embodiments, the report identifies a disease or disorder or a susceptibility thereof of the patient 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 85%, at least about 90%, at least about 95%, or at least about 100%.

[0295]

[0257] In some embodiments, the report identifies if the patient is likely to respond to a treatment comprising administration of a drug described herein. In some embodiments, the report identifies if the patient is likely to respond to a treatment comprising administration of a drug directed for the treatment of a disease or disorder as described herein.

[0296]

[0258] In some embodiments, the report identifies an effectiveness of the treatment of the patient as compared to the progression of a disease or disorder. In some embodiments, the report identifies the effectiveness of the treatment as described herein. In some embodiments, the report identifies the progression of a disease or disorder.

[0297]

[0259] In some embodiments, treating, preventing, or inhibiting disease or disorder in a patient comprises any of the methods described herein. In some embodiments, the methods of treating, preventing, or inhibiting a disease or disorder in a patient involves identification of a phenotype. In some embodiments, the phenotype is associated with a disease or disorder, an organ involvement, a medication response, a treatment response, a treatment target, a molecular endotype, or any combination thereof. In some embodiments, the methods of treating, preventing, or inhibiting a disease or disorder in a patient involve the identification of a molecular endotype of a disease or disorder. In some embodiments, methods of treating, preventing, or inhibiting disease or disorder in a patient comprises administration of a drug described herein. In some embodiments, methods of treating, preventing, or inhibiting disease or disorder in a patient comprises administration of a treatment to the patient described herein. In some embodiments, methods of treating, preventing, or inhibiting disease or disorder in a patient comprises parenteral administration of a drug to the patient. In some embodiments, methods of treating, preventing, or inhibiting disease or disorder in a patient comprise administration for at least zero weeks, 16 weeks, and 52 weeks, at least 1 year, at least 2 years, at least 3 years, at least 4 years, at least 5 years, at least 6 years, at least 7 years, at least 8 years, at least 9 years, 10 years, at least 15 years, at least 20 years, at least 30 years, at least 35 years, at least 40 years, at least 45 years, at least 50 years, or at least the patient lifespan. In some embodiments, the treatment is adjusted as a function of the gene expression data. In some embodiments, the gene expression data is used to identify a drug for the treatment of the disease or disorder. In some embodiments, the rate of treatment administration is adjusted as a function of the drug for the treatment of the disease or disorder. In some embodiments, the rate of treatment administration is adjusted as a function of the disease or disorder in a patient. In some embodiments, the rate of treatment administration is adjusted as a function of the progression of the disease or disorder.

[0298]

[0260] Also described herein are methods for treating a disease or disorder in a patient, the methods comprising predicting a clinical outcome of the disease or disorder. In some embodiments, the clinical outcome is associated to the progression of the disease or disorder. In some embodiments, the clinical outcome is associated to preventing a disease or disorder. In some embdoiments, the clinical outcome is associated to inhibiting the disease or disorder. In some embodiments, the methods of treating, preventing, or inhibiting a disease or disorder in a patient involves identification of a phenotype.

[0261] In some embodiments, treating, preventing, or inhibiting disease or disorder in a patient comprises methods for predicting a clinical outcome as described herein. In some embodiments, the clinical outcome comprises a treatment. In some embodiments, the treatment comprises administration of a drug for the treatment of a disease or disorder in a patient. In some embodiments, the treatment comprises administration of a drug to the patient as described herein. In some embodiments, the treatment comprises parenteral administration of a drug to the patient as described herein. In some embodiments, the treatment is adjusted as a function of the gene expression data generated from assaying an isolated biological sample from a patient. In some embodiments, the treatment is adjusted as a function of the gene expression data. In some embodiments, the gene expression data is used to identify a drug for a treatment of a disease or disorder. In some embodiments, the drug is selected from the group consisting of: an immunoregulator, an immunosuppressant, a steroid, an anti-inflammatory, a JAK inhibitors, a TNF inhibitors, an etanercept, an ustekinumab, a brodalumab, a dupilumab, a brepocitinib, a guselkumab, a secukinumab, a baricitinib, a corticosteroid, a nonsteroidal anti-inflammatory drug (NSAID), a tofacitinib, a upadacitinib, a deucravacitinib, a brepocitinib, a disease-modifying antirheumatic drug (DMARD), a conventional synthetic DMARD (csDMARD), a targeted synthetic DMARD (tsDMARD), a biologic DMARD (bDMARD), a biologic treatment, a TYK2 inhibitor, a TYK2 / JAK inhibitor, a combination inhibitor, a monoclonal antibody, an anti-TNF biologic, anti-IL-4 biologic, anti-IL-6 biologic, anti-IL-17 biologic, anti-IL- 12 / 23 biologic, and anti-CD28 biologic, and any combination thereof.

[0299]

[0262] In some embodiments, the method for predicting the clinical outcome of the disease or disorder of the patient comprises defining a signature that is predictive of transcript levels that indicate the clinical outcome of the disease or disorder of the patient. In some embodiments, defining a signature as described herein comprises a comparison of the data set comprising the gene expression data of a first patient in a first patient population to the gene expression data of a second patient in the first patient population. In some embodiments, the method for predicting the clinical outcome of the disease or disorder of the patient comprises electronically outputting a report detailing the signature as described herein. In some embodiments, the signature is: (i) a transcriptomic signature; (ii) predictive of clinical outcomes comprising a treatment of a disease or disorder; or (iii) predictive of clinical outcomes of a disease or disorder of a patient from gene expression data and clinical data. In some embodiments, the signature is: (i) a transcriptomic signature; (ii) predictive of clinical outcomes comprising a treatment of a disease or disorder; or (iii) predictive of clinical outcomes of a disease or disorder of a patient from gene expression data and clinical data, as detailed in FIG. 1-FIG. 20. In some embodiments, the method for predicting the clinical outcome of the disease or disorder comprises assaying samples from at least two subsets of patients, with each subset corresponding to a different disease phenotype of an established disease or disorder. In some embodiments, the first patient in the first patient population comprises a different disease phenotype than the second patient in the first patient population. In some embodiments, the different disease phenotypes respond to a different treatment. In some embodiments, the different disease phenotypes correlate to a different treatment group. In some embodiments, the report comprises data used to define a phenotype. In some embodiments, the phenotype comprises a disease or disorder, an organ involvement, a medication response, or any combination thereof.

[0300]

[0263] In some embodiments, the methods, systems, devices and compositions described herein are used for treating, preventing, or inhibiting a disease or disorder in a patient. In some embodiments, the disease or disorder is a chronic condition, a chronic condition, an inflammatory condition, an autoimmune condition, an arthritis, a rheumatoid arthritis (RA), an early inflammatory arthritis (EIA), an inflammatory arthritis, a psoriatic arthritis (PSA), a lupus arthritis, a rhupus, an osteoarthritis, a non-inflammatory arthritis, a posi-inflammatory arthritis, or combinations thereof. Exemplary diseases and syndromes comprise but are not limited to the diseases and syndromes listed in the section below.

[0301]

[0264] In some embodiments, methods of treatment described herein are associated to at least one treatment target associated with a disease or disorder described herein. In some embodiments, the disease or disorder comprises a disease or disorder selected from the section below.

[0302]

[0265] In some embodiments, the method comprises selecting, recommending and / or administering a treatment to the patient based at least in part on the classification of the disease or disorder of the patient. In some embodiments, the method comprises administering a treatment to the patient based at least in part on the classification of the disease or disorder of the patient. In some embodiments, the method comprises selecting a treatment for the patient based at least in part on the classification of the disease or disorder of the patient. In some embodiments, the method comprises recommending a treatment to the patient based at least in part on the classification of the disease or disorder of the patient. In some embodiments, the treatment for disease or disorder is configured to treat, reduce a severity of, and / or reduce a risk of having the disease or disorder. In some embodiments, the treatment for disease or disorder comprises a drug targeting one or more genes in a gene module correlated with the disease or disorder. In some embodiments, the treatment for disease or disorder is comprises one or more treatment for the disease or disorder. In some embodiments, the treatment for the disease or disorder comprises a drug targeting one or more genes in a gene moduleln some embodiments, the drug targeting one or more genes in a gene module (e.g., a significant gene module, a gene module from TABLE 4) enriched in the sample. In some embodiments, the drug targeting one or more genes in a gene module (e.g., a significant gene module, a gene module from TABLE 4) enriched in an isolated biological sample from a patient. In some embodiments, the treatment comprises pharmaceutical composition.

[0303]

[0266] In some embodiments, the treatment of a disease or disorder comprises the administration of a drug directed to the treatment targets of the disease or disorder, as appropriate. In some embodiment, the treatment of a disease or disorder comprises the administration of a drug selected from: an immunoregulator, an immunosuppressant, a steroid, an anti-inflammatory, a JAK inhibitor, a TNF inhibitor, an etanercept, an ustekinumab, a brodalumab, a dupilumab, a brepocitinib, a guselkumab, a secukinumab, a baricitinib, a corticosteroid, a nonsteroidal antiinflammatory drug (NS AID), a tofacitinib, a upadacitinib, a deucravacitinib, a brepocitinib, a biologic disease-modifying anti-rheumatic drug (DMARD), a conventional synthetic DMARD (csDMARD), a targeted synthetic DMARD (tsDMARD), a biologic DMARD (bDMARD), a biologic treatment, a TYK2 inhibitor, a TYK2 / JAK inbibitorinhibitor, a combination inhibitor, a monoclonal antibody, an anti-TNF biologic, anti -IL-4 biologic, anti -IL-6 biologic, anti-IL-17 biologic, anti-IL- 12 / 23 biologic, and anti-CD28 biologic, or combinations thereof.

[0304]

[0267] In some embodiments, the treatment for the disease or disorder comprises the administration of a drug selected fronran IFN inhibitor, a neutrophil function inhibitor, a monocyte inhibitor, an IL-1 inhibitor, an TNF inhibitor, T cell inhibitor, a cell cycle inhibitor, a neurotransmitter uptake inhibitor, a neurotransmitter uptake inhibitor, B cell inhibitor, a plasma cell inhibitor, an Ig chains inhibitor, neuromuscular pathways inhibitor, or any combination thereof. In some embodiments, the treatment for the disease or disorder comprises anifrolumab, deucravacitinib, adalimumab, certolizumab pegol, etanercept, golimumab, inflximab. palbociclib, ribociclib, abemaciclib, Anakinra, Canakinumab, Dasatinib, Apremilast, Roflumilast, belimumab, rituximab, obinutuzmab, ineilizumab, ocrelizumab, ofatumumab, Mycophenolate, Bortezomib, Carfilzomib, Ixazomib, Daratumumab, Isatuximab, Elotuzumab, or combinations thereof.

[0305]

[0268] In some embodiments, the treatment of the disease or disorder comprises AG-879, Aloisine, Alvocidib, Aminopurvalanol A, Amiodarone, Amiselimod, Amrinone, Arachidonyltrifluoromethane, Arcyriaflavin A, Arsenic Trioxide, AT-7519, Atorvastatin, Axitinib, Batimastat, Bisindolylmaleimide, Bortezomib, Briciclib, Cabozantinib, Cediranib, Cenerimod, Chlorpromazine, Cinnarizine, Cyclosporin A, Doxycycline, Entrectinib, Felodipine, Fingolimod, Flunarizine, GW-441756, HNHA, Ibudilast, Ilomastat, Lavendustin A, Lenvatinib, Lestaurtinib, Linifanib, Mepacrine, Mibefradil, Milrinone, Mocravimod, Nifedipine, Nimesulide, Nitrendipine, Nomifensine, Oxindole-I, Ozanimod, Palbociclib, Pazopanib, PHA-793887, Purvalanol A, Ramucirumab, Riboci clib, RO-3306, Roscovitine, Simvastatin, Siponimod, Sirolimus, Sorafenib, SSR-69071, Sunitinib, Tacrolimus, Tamoxifen, Tivozanib, Trequinsin, Vandetanib, Zardaverine, Gabexate, Omalizumab, Pepstatin, PHCCC, Cediranib, ENMD-2076, GW-501516, Linifanib, Quizartinib, Roscovitine, Acetyl-Famesyl-Cysteine, Aminopurvalanol A, Diclofenac, Midostaurin, Palbociclib, Rosiglitazone, Sirolimus, Sorafenib, Sunitinib, Lenvatinib, Venetoclax, Pexidartinib, Pioglitazone, Lestaurtinib, Aloisine, RO-3306, TCS-359, Dorsomorphin, GSK-0660, GTP-14564, GW-1929, JTE-013, Purvalanol A, T-0070907, Troglitazone, HLI-373, JNJ-26854165, Linifanib, NUTLIN-3, Serdemetan, Axitinib, Celecoxib, Chlortalidone, Diclofenamide, Docetaxel, Nilotinib, Paclitaxel, Sunitinib, Vinorelbine, MDM2 Inhibitor, GANT-58, IWR-l-Endo, PHCCC, Valdecoxib, Givinostat, Pegpleranib, Dasatinib, Midostaurin, Nilotinib, Pazopanib, ...

Claims

1. CLAIMS1. A method comprising: assaying an isolated biological sample from a patient to generate a data set comprising gene expression data, the assaying comprising:(a) performing an analysis with a microarray thereby measuring a concentration of a nucleic acid sequence from the biological sample or an amplicon thereof;(b) performing an RNA-Seq analysis to analyze a transcriptome of a biological sample by sequencing a complementary DNA (cDNA) synthesized from a nucleic acid sequence (RNA) from the biological sample or an amplicon thereof; or(c) performing quantitative polymerase chain reaction (qPCR) to measure an enrichment of a nucleic acid sequence from the biological sample or an amplicon thereof; and using a computer comprising a non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to run an application for identifying and comparing (i) the gene expression data generated from assaying the isolated biological sample to (ii) a reference gene expression data from one or more gene modules; electronically outputting a report detailing a comparison of (i) the gene expression data set generated from assaying the isolated biological sample to (ii) the reference gene expression data set from one or more gene modules; wherein the report:(i) identifies an immunological state of the patient at an accuracy of at least about 70%;(ii) identifies a disease or disorder or a susceptibility thereof of the patient at an accuracy of at least about 70%;(iii) identifies if the patient is likely to respond to a treatment comprising administration of a drug comprising an immunoregulator, an immunosuppressant, a steroid, an anti-inflammatory, a JAK inhibitors, a TNF inhibitors, an etanercept, an ustekinumab, a brodalumab, a dupilumab, a brepocitinib, a guselkumab, a secukinumab, a baricitinib, a corticosteroid, a nonsteroidal antiinflammatory drug (NS AID), a tofacitinib, a upadacitinib, a deucravacitinib, a brepocitinib, a disease-modifying anti-rheumatic drug (DMARD), a conventional synthetic DMARD (csDMARD), a targeted synthetic DMARD (tsDMARD), a biologic DMARD (bDMARD), a biologic treatment, a TYK2 inhibitor, a TYK2 / JAK inhibitor, a combination inhibitor, a monoclonal antibody, an anti-TNF biologic, anti-IL-4 biologic, anti -IL-6 biologic, anti-IL-17 biologic, anti-IL-12 / 23 biologic, anti-CD28 biologic, or combinations thereof; and / or(iv) identifies an effectiveness of the treatment of the patient as compared to the disease or disorder, or a progression of the disease or disorder; wherein: one or more gene modules comprise genes with similar gene expression data, similar gene expression profile, or coexpression; one or more gene modules comprise genes associated with a disease or disorder; each of the one or more gene modules comprise 2 or more genes as shown in TABLE 4; each of the one or more gene modules are associated with a different disease or disorder, treatment, process, or cell type as shown in FIG. 1-FIG. 20; or the disease or disorder is a chronic condition, an inflammatory condition, an autoimmune condition, a dermatological condition, a skin disease, a psoriasis, plaque psoriasis, guttate psoriasis, inverse psoriasis, scalp psoriasis, nail psoriasis, a dermatitis, an atopic dermatitis, stasis dermatitis, contact dermatitis, allergic contact dermatitis, irritant contact dermatitis, neurodermatitis, perioral dermatitis, seborrheic dermatitis, atopic dermatitis, eczema, or combinations thereof; the isolated biological sample is a whole blood (WB) sample, a peripheral blood mononuclear cell (PBMC) sample, a blood sample, a tissue sample, a purified cell sample, or combinations thereof; and optionally wherein the method comprising assaying an isolated biological sample from a patient further comprises purifying the isolated biological sample to obtain a purified cell sample.

2. The method of claim 1, wherein a gene module of the one or more gene modules comprises 2 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 31 or more, 32 or more, 33 or more, 34 or more, 35 or more, 36 or more, 37 or more, 38 or more, 39 or more, 40 or more, 41 or more, 42 or more, 43 or more, 44 or more, 45 or more, 46 or more, 47 or more, 48 or more, 49 or more, or 50 or more genes associated with the gene module.

3. The method of claim 1, wherein a gene module of the one or more gene modules comprises at least 2, 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, or 50 genes associated with a biological pathway.

4. The method of claim 1, wherein the disease or disorder is the chronic condition.

5. The method of claim 1, wherein the disease or disorder is the inflammatory condition.

6. The method of claim 1, wherein the disease or disorder is the autoimmune condition.

7. The method of claim 1, wherein the disease or disorder is the dermatological condition.

8. The method of claim 1, wherein the disease or disorder is the skin disease .

9. The method of claim 1, wherein the disease or disorder is the psoriasis.

10. The method of claim 1, wherein the one or more gene modules are associated with a molecular endotype of a disease or disorder.

11. The method of claim 1, wherein the one or more gene modules are associated with a phenotype.

12. The method of claim 1, wherein the treatment comprises administration of a drug to the patient.

13. The method of claim 1, wherein the treatment comprises parenteral administration of a drug to the patient.

14. The method of claim 1, wherein the treatment comprises oral administration of a drug to the patient.

15. The method of claim 1, wherein the treatment comprises administration for at least zero weeks, 16 weeks, and 52 weeks, at least 1 year, at least 2 years, at least 3 years, at least 4 years, at least 5 years, at least 6 years, at least 7 years, at least 8 years, at least 9 years, 10 years, at least 15 years, at least 20 years, at least 30 years, at least 35 years, at least 40 years, at least 45 years, at least 50 years, or at least the patient lifespan.

16. The method of claim 1, wherein the treatment is adjusted as a function of the gene expression data.

17. The method of claim 1, wherein the gene expression data is used to identify a drug for the treatment of the disease or disorder.

18. The method of claim 1, wherein the report comprises nucleic acid sequencing data, transcriptome data, genome data, epigenetic data, proteome data, metabolome data, virome data, metabolome data, methylome data, lipidomic data, lineage-ome data, nucleosomal occupancy data, a genetic variant, a gene fusion, an indel, or combinations thereof.

19. The method of claim 1, wherein the report comprises different formats.

20. The method of claim 1, wherein the report comprises data from different sources, different studies, or combinations thereof.

21. The method of claim 20, wherein the data is used to define a phenotype.

22. The method of claim 21, wherein the phenotype is associated with a disease or disorder, a progression of a disease or disorder, a trajectory of a disease or disorder, a deterioration, a disability, an organ involvement, a medication response, a treatment response, a treatment target, a treatment, a treatment recommendation, a molecular endotype, or combinations thereof.

23. The method of claim 21, wherein the phenotype is associated with a treatment adjusted as a function of a progression of a disease or disorder.

24. The method of claim 22, wherein the progression of the disease or disorder comprises rate of deterioration of the patient over time.

25. The method of claim 22, wherein the phenotype is associated with a treatment adjusted as a function of a trajectory of a disease or disorder.

26. The method of claim 25, wherein the trajectory of the disease or disorder comprises a sequence of diagnoses over time.

27. The method of claim 25, wherein the trajectory of the disease or disorder comprises patient decline comprising slow decline, gradual decline, stair step decline, or rapid decline.

28. The method of claim 21, wherein the phenotype is associated with a treatment adjusted as a function of the progression of the disease or disorder.

29. The method of claim 21, wherein the phenotype is associated with a treatment adjusted to comprise a different rate of administration, a different frequency of administration, administration of a different a drug, administration of more than one drug, administration of different combinations of drugs, or combinations thereof.

30. The method of claim 1, wherein the one or more gene modules are a plurality of gene modules.

31. The method of claim 1, wherein the method identifies more than one disease or disorder.

32. A kit for performing the method of claim 1-31, the kit comprising a structural component as well as a composition component, wherein the composition component is a reaction mixture comprising an isolated biological sample from a patient and composition components and / or reagents for performing the assaying.

33. The kit of claim 32, wherein the structural component comprises a sample interface.

34. The kit of claim 32, wherein the structural component comprises a computer comprising a non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to run an application for identifying andcomparing (i) the gene expression data generated from assaying the isolated biological sample to (ii) the reference gene expression data from one or more gene modules.

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