Intelligent system and methods for therapeutic target identification

Abstract

To provide a system and computer-implemented method for uncovering genetic drivers of disease in neurodegeneration and other central nervous system (CNS) diseases for novel therapeutic target identification.SOLUTION: The process can start with a first integrated, quantitative "deep" imaging phenotype, that accurately reflects disease at a given time-point (cross-sectionally) and returns candidate single nucleotide polymorphisms (SNPs) and / or genes. The candidate SNPs / genes are further validated, by using a second machine learning and / or artificial intelligence (Al)-based image analysis operation to assess clinical response, and may include gene expression profiling, and target plausibility analysis including pathway mapping. A set of candidate SNPs / genes may be constructed to accurately predict the first imaging phenotype with a deep learning model from the SNPs / genes, serving as an additional validation step.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Neurodegenerative and other central nervous system (CNS) diseases require new targets for therapeutic development This is especially true as the field moves away from symptomatic approaches toward disease modification and prevention. In Alzheimer's disease (AD), almost all clinical trials to date have Because of the failure of previous trials, discovering and targeting new genetic drivers of disease is of great interest. It has a heart.

[0002] Artificial intelligence (AI) and machine learning-based approaches can aid and streamline drug discovery and development. The industry is, for example, focused on deep learning in drug discovery and development. Applying learnings to “big data” sources such as electronic health record (EHR) data However, the quality of these "big data" sources varies widely. Moreover, predictions made by AI may be difficult to explain and / or may be incorrect. Therefore, the patterns detected and the predictions made are transparent and valid. There is a trend towards explainable AI. Data sources are even harder to come by, and practical applications require "few-shot learning." A "learning by doing" approach is needed; this could require thousands or even millions of training opportunities. In stark contrast to certain deep learning approaches, few-shot learning systems , with much fewer opportunities for training, how infants learn new tasks has been associated with.

[0003] In this way, information can be extracted from limited data sources to support biopharmaceutical research and development. This paper will help researchers understand the genetic drivers of diseases in the central nervous system and beyond, in a transparent and easily understood way. There is an unmet need for smarter, more intelligent systems. Summary of the Invention

[0004] This disclosure provides a novel approach to the identification of novel therapeutic targets for neurodegeneration and We are working to identify the genetic drivers of disease in schizophrenia and other central nervous system (CNS) disorders. According to one aspect of an embodiment, the present invention provides a method for detecting a plurality of stimuli, the method comprising: This paper first describes a method that starts from an integrated quantitative "deep" imaging phenotype. , which accurately reflects the disease at a given time point (cross-sectional) and the candidate single nucleotide polymorphisms (SNPs) and / or genes. The candidate SNPs / genes are at least partially determined by a second machine learning and and / or artificial intelligence (AI)-based image analysis procedures to assess clinical response. Further analysis includes gene expression profiling and target validation analysis including pathway mapping to obtain A set of candidate SNPs / genes will be constructed to serve as an additional validation step. The first imaging phenotype was generated using a deep learning model from the aforementioned SNPs / genes. It can be predicted accurately.

[0005] Exemplary embodiments of the present disclosure discover therapeutic targets in just a few hundred samples and thousands more. achieves comparable performance to other gene discovery / risk prediction approaches requiring 100 samples The present invention provides a computer technique or use in Alzheimer's disease research for:

[0006] Some embodiments herein provide improvements in the field of image genetics and AI, and in one embodiment In one embodiment, the computer-implemented method includes: Start with imaging phenotypes, which are single measurements that describe disease at a given time point (cross-sectionally). Accurately reflecting, for example, genome-wide association studies of quantitative trait loci (QTL GWAS) returning candidate single nucleotide polymorphisms (SNPs) and / or genes.

[0007] Other QTL GWAS approaches to date have used QTL-based gene sequences that are typically found in, for example, medical records or EHRs. An example of a quantitative measurement used in the conventional approach is the noise level. Many discrete cognitive test results, or global amyloid measures, or possibly tau-positive These traditional approaches may include Among other limitations, the authors note that the prediction of the conversion of early disease stages to AD is difficult. At best, GWAS solutions have had limited success due to the lack of precision in QTL. In contrast to case-control analyses, case-control analyses are limited by uncertainty about what constitutes a "case" of AD. This can be complicated by the fact that these case-control analyses rely on informal family history information. There are many reasons for this, including the fact that newer approaches for target discovery are still limited. The proposed approach is aimed at aggregating brain bank samples and, for example, RNA sequencing transfection. This is an analysis of the brain bank data by cryptomics. However, the time required for sample preparation and sample capture after death may be a significant factor in determining the severity of the disease. As such, it may reveal meaningful new therapeutic targets in AD. That hasn't been done yet.

[0008] In contrast to these and other conventional approaches, positron emission tomography (PET) and Molecular multimodal imaging, including voxel-based quantification of magnetic resonance imaging (MRI) Zing can encode local spatial information and capture molecular events in vivo, It is more suitable.

[0009] In some embodiments, a computer-aided method for identifying genetic biomarkers for a condition is provided. The method may include: for a state of interest, determining a plurality of pairs having the state. For each sample, perform quantitative image analysis of the phenotypic images to obtain the first accurate quantitative phenotype. performing quantitative genomic analysis on each of a plurality of subjects or a plurality of different subjects; Quantitative imaging and genomic analysis will provide insight into the condition of multiple subjects. obtaining at least one candidate gene biomarker; obtaining at least one candidate gene biomarker To validate the biomarkers, clinical responses to at least one candidate gene biomarker were identified. predicting an answer to a question about a condition based on at least one candidate gene biomarker; Identifying at least one therapeutic target, wherein at least one treatment for the condition is identified. The therapeutic target is biologically associated with at least one candidate gene biomarker; and Generate a report including at least one identified therapeutic target for the condition. In one embodiment, performing quantitative genomic analysis includes determining genome-wide relationships between quantitative trait loci. This includes performing association analyses (QTL GWAS).

[0010] In some embodiments, the method further comprises: validating at least one candidate gene biomarker. and predicting clinical response to at least one candidate gene biomarker to In some embodiments, the prediction of clinical response may include determining a validation metric. The present invention relates to a method for investigating at least one biological pathway associated with at least one genetic biomarker. In some embodiments, the predicted clinical response is determined by determining whether at least one At least one biologically active protein in a biological pathway associated with the genetic biomarker. This is carried out to inhibit quality.

[0011] In some embodiments, the method may include at least one of the following: Obtaining phenotypic images, where the images accurately represent the state at a given time point; quantitative representations Obtaining phenotypic accuracy of about 85% or greater for the present type; detecting disease as a condition; defining disease activity across the disease spectrum; or Performing a standardized uptake value ratio (SUVR) analysis using the phenotypic images of interest.

[0012] In some embodiments, performing quantitative image analysis of the phenotypic images comprises: At least one of the following: voxel-based amyloid standardized uptake ratio (SU Calculate voxel-based dopamine transporter single photon emission (VR) from the images. Calculating Quantitative CT Tomography (DAT-SPECT) Quantitation from Voxel-Based Alpha Images -synuclein ligand binding; or local serotonin receptor binding from the images. Calculate the Gand coupling.

[0013] In some embodiments, the images include positron emission tomography (PET) and magnetic resonance imaging. The following methods are selected: magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and their combinations. It is selected.

[0014] In some embodiments, predicting the clinical response comprises determining at least one of the following: Includes: performing hippocampal masked voxel-based tau SUVR with genotype quantification To quantify genotypes using hippocampal voxel-based tau SUVR recording perform memory tracking determination; perform voxel-based tau quantification within the hippocampus; performing tau imaging to track clinical response; or performing clinical response tracking measurements. In some embodiments, predicting the clinical response includes at least one of the following: : performing tremor quantification; or performing automated tremor quantification, In some embodiments, the quantification of tremor is optionally by genotype. To calculate the serotonin receptor ligand binding on a voxel basis from the images, include.

[0015] In some embodiments, the method comprises: The method may further include performing a prediction of phenotype from genotype based on the markers. In one embodiment, the prediction of the phenotype includes at least one of the following: single nucleotide polymorphisms (SNPs) ) Phenotype prediction from deep learning models; from positron emission tomography (PET) images Quantitative phenotyping; voxel-based amyloid SUVR using deep learning SNP models Predicting voxel-based DAT-SPECT quantification using deep learning SNP models Predicting voxel-based α-synuclein ligand binding using deep learning SNP models or predicting local serotonin receptor ligand binding by deep learning SNP models. Predicting bond binding.

[0016] In some embodiments, the method may include at least one of the following: at least one candidate gene biomarker on the Analyzing a biological pathway having a child biomarker; or Comparing the child biomarkers to biological pathways associated with the condition.

[0017] In some embodiments, the condition is a set of medical conditions that correlate with each other in a subject. A syndrome with characteristic signs and symptoms; a pathophysiological response to factors external or internal to the subject. or a disorder that disrupts the normal bodily structure and / or function of a subject. In an embodiment, the condition is selected from the group consisting of Alzheimer's disease, Parkinson's disease, and major depressive disorder (MDD). are selected.

[0018] In some embodiments, the computer-implemented method may include: Perform quantitative phenotypic analysis; genome-wide association studies of quantitative trait loci (QTL To conduct GWAS (Genomic Wave Assays); quantitative imaging and quantitative genomic analyses for multiple subjects obtaining at least one candidate gene biomarker for a condition in To validate a candidate gene biomarker, at least one candidate gene biomarker predicting clinical response to rhabdomyolysis; and predicting phenotypes using deep learning SNP models To carry out the following.

[0019] In some embodiments, the computer-implemented method may include: Calculating voxel-based DAT-SPECT quantification from CT and T1 MRI images ;Performing quantitative trait locus genome-wide association studies (QTL GWAS);Quantitative and determining whether at least one of the conditions is present in a plurality of subjects from quantitative image analysis and quantitative genomic analysis. To obtain four candidate gene biomarkers; to compare tremor quantification by genotype; To predict voxel-based DAT-SPECT quantification by trained SNP models.

[0020] In some embodiments, the computer-implemented method may include: Calculating voxel-based α-synuclein ligand binding from T1 and T2 MRI images and; to perform quantitative trait locus genome-wide association studies (QTL GWAS); Quantitative imaging and quantitative genomic analysis provide at least a quantifiable profile of the condition in multiple subjects. Obtaining a single candidate gene biomarker; comparing tremor quantification by genotype; and and predicting voxel-based α-synuclein ligand binding using deep learning SNP models To do.

[0021] In some embodiments, the computer-implemented method may include: and calculating regional serotonin receptor ligand binding from T1 MRI images; quantitative form Performing quality locus genome-wide association studies (QTL GWAS); a quantitative imaging approach and identifying at least one candidate for the condition in a plurality of subjects from the quantitative genomic analysis and quantitative genomic analysis. Obtaining genetic biomarkers; voxel-based analysis using PET and T1 MRI images Calculate serotonin receptor ligand binding; and localize it using deep learning SNP models. To predict specific serotonin receptor ligand binding.

[0022] In some embodiments, the computer-implemented method may include: and calculating voxel-based amyloid SUVR from T1 MRI images; quantitative form Performing quality locus genome-wide association studies (QTL GWAS); a quantitative imaging approach and identifying at least one candidate for the condition in a plurality of subjects from the quantitative genomic analysis and quantitative genomic analysis. Obtaining genetic biomarkers: hippocampal masked voxel-based tau SUVR was Comparing genotypes; voxel-based amyloid SUVs using deep learning SNP models and a report containing predicted voxel-based amyloid SUVR. To generate a

[0023] In some embodiments, the method for detecting amyloid-positive or early Alzheimer's disease A computer-implemented method for training an artificial neural network includes: You can view: Corresponding positron emission tomography (PET) and magnetic resonance images from the database Collect a set of MRI images and corresponding genotype data; PET images and We calculated voxel-based amyloid SUVR from T1 and T2 MRI images to predict early Alzheimer's disease. Obtaining accurate quantitative phenotypes of Immer's disease; quantitative phenotype and corresponding genotype data generating a training set; and determining a quantification from the genotype data using the training set. and performing training of an artificial neural network to predict the target phenotype. In one embodiment, the artificial neural network comprises at least SNP ids 1-32, or a subset of SNP ids 1-32, and optionally to predict a quantitative phenotype It was trained using genotype data, including SNP id 33 in Table 1. In some aspects, training includes providing training to operate in the methods described herein. The subset may include a subset having a sufficient number of SNPs. The subset may include NP id 1-32 , at least 10 SNPs, at least 15 SNPs, at least 20 SNPs The subgroup may include at least 25 SNPs or at least 30 SNPs. It should be noted that a larger number of SNPs in the set may improve performance. be.

[0024] In some embodiments, a system for identifying genetic biomarkers for a condition includes: It may include: a deep phenotyping unit; a QTL downstream of a deep phenotyping unit. GWAS analysis unit; and clinical response validation downstream of QTL GWAS analysis unit In some embodiments, the system comprises a genetic test that is downstream of the clinical response validation unit. In some embodiments, the system includes a clinical response validation unit. Downstream, and optionally downstream, of the gene expression analysis unit, is a pathway mapping unit. In some embodiments, the system includes a clinical response verification unit downstream of the clinical response verification unit. Also optionally downstream of the gene expression analysis unit, and also optionally a pathway mapping unit. In some embodiments, the system further comprises a phenotype prediction unit downstream of the coding unit. , including a toolset system which may include one or more units selected from the following: Pattern and / or trend analysis unit;Statistical analysis unit;Text mining validation solution analysis unit; New Chemical Substances (NCE) prediction unit; or reporting unit.

[0025] In some embodiments, the system may be configured according to one or more of the following: In some embodiments, the deep phenotyping unit performs at least one of the following: It is designed to: perform accurate quantitative phenotypic analysis; PET and T 1 Calculating voxel-based amyloid SUVR from MRI images; SPECT and Calculating voxel-based DAT-SPECT quantification from T1 and T2 MRI images;PE Calculating voxel-based α-synuclein ligand binding from T and T1 MRI images or to measure local serotonin receptor ligand binding from PET and T1 MRI images. In some embodiments, the QTL GWAS analysis unit is a quantitative trait locus It is configured to perform genome-wide association studies (QTL GWAS) of several In one embodiment, the clinical response verification unit is configured to perform at least one of the following: Consists of: at least one candidate gene biomarker to validate the candidate gene biomarker; Predicting clinical response to another candidate gene biomarker; hippocampal masked body To compare cluster-based tau SUVR by genotype; to compare tremor quantification by genotype or voxel-based serotonin receptor spectroscopy using PET and T1 MRI images. Calculating somatic ligand binding.

[0026] In some embodiments, the system may include one or more of the following: automation configured to perform gene expression profiling, either automated or non-automated one of the genes identified by the gene expression analysis unit; Pathway Mapping Unit configured to map multiple biological pathways; Deep Learning and using the learned model to predict a phenotype identified by the deep phenotyping unit. or phenotypes constructed to predict new phenotypes for new investigations of new conditions. A prediction unit, optionally used in conjunction with a deep phenotyping unit, for predicting phenotypic patterns a pattern and / or trend analysis unit configured to determine the pattern and / or trend of the method; a statistical analysis unit configured to perform statistical analysis of any data or metrics; to determine the relevance of therapeutic targets by text mining and / or and determining a chemical modulator of a biological entity of a biological pathway associated with Text Mining Validation Analysis Unit; New Chemical Substances That Can Modulate Therapeutic Targets (N a new chemical entity (NCE) prediction unit configured to design or generate a new chemical entity (NCE); or The reporting unit is configured to generate a report. [Brief description of the drawings]

[0027] Embodiments of the present disclosure are illustrated by way of example and not by way of limitation in the following figures: This disclosure can be better understood by: [Figure 1] FIG. 1 is an example flowchart of an efficient, computer-implemented method for uncovering genetic drivers of disease in neurodegenerative and other CNS disorders from much less human data for novel therapeutic target identification. [Diagram 2] FIG. 1 is an example flowchart of an efficient, computer-implemented method for uncovering genetic drivers of Alzheimer's disease from much less human data for novel therapeutic target identification. [Diagram 3]FIG. 1 is an example flowchart of a computer-implemented method for identifying genetic drivers of Parkinson's disease using voxel-based dopamine transporter single-photon emission computed tomography (DAT-SPECT) deep imaging phenotype and automated tremor quantification for clinical response assessment calculated from DAT-SPECT and MRI. [Figure 4] FIG. 1 is an example flowchart of a computer-implemented method for identifying genetic drivers of Parkinson's disease using automated tremor quantification for voxel-based deep imaging phenotype and clinical response assessment calculated from alpha-synuclein PET and MRI. [Diagram 5] FIG. 1 is an example flowchart of a computer-implemented method for identifying disease genetic drivers of Major Depressive Disorder (MDD) using deep imaging phenotypes calculated from regional serotonin receptor ligand binding from PET and T1 MRI, and voxel-based serotonin receptor ligand binding from PET and TI MRI for clinical response assessment. [Figure 6] FIG. 1 illustrates the main components and data flow of an exemplary intelligent system for therapeutic target identification in drug discovery. [Figure 7] Figure 1 shows high accuracy (> / =85%) of the new National Institute on Aging and Alzheimer's Association (NIA-AA) "ATN" (beta-amyloid deposits, pathological tau, and neurodegeneration) criteria for quantitative "deep" phenotypic target identification, here as an example voxel-based standardized uptake value ratios (SUVR) calculated from single amyloid PET and T1 MRI scans, and discrimination between different ATN biomarker profile groups. [Figure 8] FIG. 1 shows preliminary results in 30 subjects for voxel-based tau PET quantification within the hippocampus to assess clinical response, associated with either tau PET or amyloid PET quantification, showing stronger correlation with the Mini-Mental State Examination (MMSE) than reported by other investigators. [Figure 9] Example of a report generated by the system for revealing genetic drivers of disease in Alzheimer's disease, here showing as an example the candidate lysophosphatidylcholine acyltransferase 2 (LPCAT2) gene / protein; suggestive association of the protective SNP rs4402561 was detected by a first precise quantitative phenotype (PETQ) and a step-down pattern was confirmed by a second clinical response follow-up measurement (PETQ TAU) (training p=0.054). [Figure 10] Example of a report generated by the system to reveal genetic drivers of disease in Alzheimer's disease, here showing the candidate LPCAT2 gene / protein as an example; from blood expression profiling we can see a highly significant step-up pattern of the LPCAT2 probe, possibly related to regulated transcripts. [Figure 11] Example report generated by the system for uncovering genetic drivers of disease in Alzheimer's disease, showing the candidate LPCAT2 gene / protein as an example; Brain bank gene expression profiling confirms a trend step-up pattern of LPCAT2 probes among young controls (yCTR), age-matched old controls (oCTR) and Alzheimer's disease subjects (AD) within the superior frontal cortex. [Figure 12] Example report generated by the system to reveal genetic drivers of Alzheimer's disease, showing the candidate LPCAT2 gene / protein as an example; Blood expression profiling of the inflammatory cytokine IL6 by rs4402561 minor allele carrier status confirms a trend step-down pattern, explaining the protection. [Figure 13]Figure 1 shows the main components and data flow in an example of phenotype prediction within an intelligent system for therapeutic target identification in drug discovery (DeepQnt); a set of candidate SNPs / genes can be constructed to predict the original imaging phenotype using a deep learning model from the discovered SNPs / genes, which can serve as an additional validation step (VCF file is shown, but BCF file can also be used). [Figure 14] Genotype-to-phenotype (PETQ SUVR) prediction using a deep learning-based system (DeepQnt) within an intelligent system for therapeutic target identification in drug discovery. Subjects with amyloid-positive PET scans (assessed by SPAP, an FDA-approved method) are labeled SPAP+. [Figure 15] Figure 1 shows the performance of an example of phenotype prediction (DeepQnt) within an intelligent system for therapeutic target identification in drug discovery; as an example, powerful computational techniques for Alzheimer's disease (AD) allow discovery of therapeutic targets with just a few hundred samples, achieving comparable performance to other gene discovery / risk prediction approaches that require thousands of samples; SPAPpos shows NC / C conversion of Mild Cognitive Impairment (MCI) to AD in a dataset with amyloid positive PET scans and long-term (>=5 years) follow-up. [Figure 16] 16 illustrates an example of an exemplary computing device 1600 (eg, a computer) that may be provided in some embodiments for performing the methods (or portions thereof) described herein. [Figure 17]Figure 1 shows a head-to-head performance comparison of an example of phenotype prediction within an intelligent system for therapeutic target identification in drug discovery (DeepQnt) against a benchmark method (Polygenic Hazard Score, PHS; Desikan et al., PLoS Medicine 2017); as an example, the computational techniques disclosed in Alzheimer's Disease (AD) allow discovery of therapeutic targets with just a few hundred samples, achieving comparable performance (here MCI to AD conversion detection) to other gene discovery / risk prediction approaches such as PHS drawn from thousands of subjects. NC / C indicates conversion from Mild Cognitive Impairment (MCI) to AD in a dataset with long-term (>=5 years) follow-up. [Figure 18] Figure 1 shows a head-to-head performance comparison of an example of phenotype prediction within an intelligent system for therapeutic target identification in drug discovery (DeepQnt) against a benchmark method (Polygenic Hazard Score, PHS; Desikan et al., PLoS Medicine 2017); as an example, the computational technique disclosed in Alzheimer's Disease (AD) is able to find therapeutic targets with only a few hundred samples and achieves comparable performance (here for detecting amyloid positivity in MCI subjects). SPAP positive indicates amyloid positive PET scan; MCI is mild cognitive impairment. [Figure 19] Figure 1 shows an example performance comparison of phenotype prediction within an intelligent system for therapeutic target identification in drug discovery (DeepQnt) for 54 MCI test cases; as an example, the disclosed computational technique in Alzheimer's disease (AD) allows therapeutic targets to be discovered with only a few hundred samples, achieving improved performance (here MCI to AD conversion detection) compared to APOE4 carrier status alone. MCI denotes mild cognitive impairment in datasets with long (>=5 years) follow-up. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] In general, the technology processes data to identify genetic information indicative of a condition, such as a disorder, disease, or syndrome. The present invention relates to a method for identifying information on a network and a system for carrying out the method. The protocol can be used to identify SNPs or other genotype information that is indicative of a condition. The method involves screening a population of subjects to identify genotypes associated with a phenotype of the condition. It provides metrics that can be used to analyze and process the data set.

[0029] In some embodiments, the protocol involves the use of time-point data on disease, referred to as cross-sectional analysis. However, the protocol requires the collection of multiple data over time, known as longitudinal analyses. A tag point may be used.

[0030] The protocol involves the collection of data points across a population of subjects for quantitative measurement of the subject's phenotype. Cross-sectional quantitative measurements of disease at that time point may provide insight into the phenotype of the condition of interest. Cross-sectional measurements can be used to analyze the early stages before symptoms appear, known as the preclinical or presymptomatic stage. This may be performed at a very early stage of the disease, such as the early or symptomatic stage.

[0031] The dataset contains a set of conditions that can be analyzed to identify quantitative phenotypes as metrics for the condition. The new ATNs provided herein can include measurements of a state, such as a measure of a patient's condition. The definitions may be used to determine a condition. For example, in the case of Alzheimer's disease (AD), Myloid and tau positivity were used to identify preclinical AD states or pre-AD, prior to the stage of full-blown dementia. Cross-sectional measurements may define the mental health status of individuals with no psychiatric disorder or with some degree of psychiatric disorder but not complete pathology. is applied to the data set of interest (e.g., Example 1). Thus, the metric is applied and the state is, for example, about 85% or greater accuracy (e.g., , defined as a precise quantitative phenotype) that is determined to be accurately identifiable by the metric. do.

[0032] In preclinical controls (i.e., asymptomatic subjects), subjects were analyzed by metrics to determine their status. Thus, subjects who are likely to develop the condition can be identified as those who have developed the condition. Once a metric is identified, it can be used to distinguish the target from the likely targets. It can predict the likelihood of developing the condition with up to 100% accuracy.

[0033] As described herein, deep phenotyping can be used to generate metrics (e.g., This allows data to be extracted up to a point where it is possible to obtain accurate quantitative phenotypes. The metric is used to measure disease progression from a pre-symptomatic state through onset to a fully developed state (e.g., Alzheimer's disease). This allows the determination of any point in the spectrum of states, up to

[0034] Once identified, the metrics can be used to validate against targets that are not specifically related to the state. The metric may be used with discovery datasets to genotype a condition. To do this, a discovery dataset (e.g., one time point per patient, cross-sectional but The time points for one subject may be different from the time points for the other subjects. At each end of the spectrum of states, the protocol identifies the genetic code associated with that state. To do this, the genotype is matched to the phenotype. Genetic analysis then identifies the S This may provide for the identification of one or more genes that may contain NP. , or deep phenotypes that can be used for a variety of subjects with a condition.

[0035] In some embodiments, the voxel-based SUVR is analyzed by genotype to obtain voxel-based SUVR. For example, step 240 of FIG. 2 may obtain voxel metrics. The new voxel metric can be used to determine the function of the hippocampus, which handles memory. The hippocampus is used because it is associated with Alzheimer's disease, such as tau. Voxel metrics may be correlated with memory, as measured by cognitive measures such as the MMSE. This can be shown in FIG. 8 as described herein, which is a voxel metric. These results indicate that the Q is a clinical response measure that tracks memory.

[0036] In some embodiments, the protocol may include: image data (e.g., deep surface Obtaining accurate quantitative phenotypes from the phenotype; using accurate quantitative phenotypes as metrics obtaining a genotype; determining voxel metrics; and and a voxel metric, which may be a response tracking metric, to genotypes such as SNPs. Combine with accurate quantitative phenotypic metrics to validate. For example, see the table below for Various embodiments disclosed herein may be used to identify hits (e.g., pSNPs) such as SNPs with lower values. Carriers of the SNPs may then be analyzed to find SNPs that are susceptible. have a condition or are protected against acquiring the condition We then identified SNP carriers and non-carriers as indicators of hippocampal status. The carrier may be analyzed for memory.

[0037] In some embodiments, deep learning can be applied to the SNP model and the data can be analyzed to A quantitative phenotypic prediction of the phenotype ... The method of obtaining SNPs may be used prior to training the SNP model. The SNP model may then be used to For example, FIG. 14 shows a SNP model that predicts the original quantitative phenotype as a further validation. Figure 1 shows the predicted values ​​versus the original quantitative phenotype based on the model.

[0038] In some embodiments, the SNP model is This model can be tested by applying the SNP model to a data set having The model can also distinguish between subjects with and without the condition based on the SNP model.

[0039] Current techniques using SNP models require large data sets with thousands of data points. Instead of requiring This allows the SNPs to be used for diagnostic purposes.

[0040] In the following description, for purposes of explanation, specific details of the present disclosure are set forth; However, the present disclosure may be more clearly understood by those skilled in the art without these specific details. It will be apparent that certain features of the exemplary embodiments of the present disclosure may be implemented. are detailed in the examples below and illustrated in Figures 1-19.

[0041] In some embodiments, FIG. 1 illustrates a method 100 for identifying genetic biomarkers for a condition. The method 100 includes: performing quantitative image analysis of the images of the phenotype to obtain a first accurate quantitative phenotype. This can also be referred to as obtaining accurate quantitative phenotypes, as shown in step 110. This step may be performed as described herein with respect to the disclosure and examples. In some embodiments, this step can be performed by acquiring an image of the subject's phenotype. The images now show the exact state at a given time point. This step allows for quantitative phenotyping. This may include obtaining a phenotypic accuracy of greater than or about 85% for a given phenotype. It may be exactly 85%, greater than 85% or about 85% (e.g., 1%, 2%, 3%, 4% or within 5%).

[0042] In some embodiments, the method 100 comprises identifying a disease, disorder, or condition from a phenotypic image of a subject. Detecting the harm or syndrome, which may be performed in step 110 or a sub-step thereof. Thus, the images may be included in one or more of the objects. The images may be processed to detect conditions such as disease states, disorders or syndromes in the elephant. The analysis and database information may be used during processing to detect the condition. In one embodiment, the method 100 performs longitudinal analysis using multiple data points over time of a disease. A quantitative analysis may be performed.

[0043] In some embodiments, the method 100 includes in step 110 or a sub-step thereof The condition activity (e.g., disease activity) across the condition spectrum (e.g., disease spectrum) that may be This may include a process for defining the behavior of each object or objects. It may be possible to identify the stage of a condition or the progression of a condition in one or more of the subjects. Therefore, subjects do not need to have the same disease state in the analysis. This allows data points to be The data points may be a single time point in a study, but may span a range of condition progressions in a population of interest. may be also possible.

[0044] In some embodiments, the method 100 comprises: The method may include performing a Supervised Real-Time Virtualization (SUVR) analysis. SUVR is known in the art. SUVR can be implemented using image data such as positron emission tomography (PET) images. It can be calculated using the above methods to provide a quantitative analysis of the state and state conditions.

[0045] FIG. 1 also illustrates a method, as in step 120, for detecting each of a plurality of objects or a plurality of different objects. The present invention relates to a method for the detection of genomic abnormalities in a mammalian animal, the method comprising: The analysis may be performed on the same subjects as in step 110, but in an independent data set. A validation analysis of the set may be performed on a different group of subjects. Quantitative genomic analysis is a method for quantitative trait locus genome-wide association studies (QTL GWA). QTL GWAS includes performing a gene expression analysis (e.g., step 120) of a gene expression vector (e.g., ... The results of the QTL GWAS can be used to identify correlations with conditions. One or more SNPs may be provided, such as a panel of SNPs to analyze.

[0046] Thus, the results of the genomic analysis may be used to identify at least one of the following conditions: Each of these may result in the identification of a single candidate gene biomarker. Information from quantitative genomic analysis can be used to identify SNPs, such as minor or major alleles, or To identify one or more candidate biomarkers, which may be genetic codes, the steps are It can be used in step 130.

[0047] The method 100 also includes performing at least one genomic sequence analysis to validate the at least one candidate gene biomarker. The method may include predicting clinical response to at least one candidate gene biomarker. This may be performed in step 140 to provide clinical response validation. can help improve the quality of the method and validate identified candidate biomarkers. This step can be carried out in a variety of protocols, which are specifically described in more detail herein. 2, as described in detail below. Thus, the predicted clinical response The present invention relates to a method for investigating at least one biological pathway associated with at least one genetic biomarker. In some embodiments, the predicted clinical response can be determined by determining whether the patient is a 100% or 200% CI, 0.01 to 1.001. At least one biologically active tag of a biological pathway associated with one genetic biomarker This clinical validation may be performed to inhibit one or more candidate biomarkers. can be used to validate the marker and to extract one or more It can be used to select validated candidate biomarkers.

[0048] In some embodiments, predicting the clinical response comprises determining whether a different process for clinical response validation is performed. In some embodiments, the clinical response validation may include a protocol as described herein. To perform hippocampal masked voxel-based tau SUVR with genotype quantification In some embodiments, the clinical response validation may include administering to the hippocampus as described herein. Perform voxel-based tau SUVR memory tracking determination with genotype quantification using In some embodiments, the clinical response validation may include performing a clinical response validation step as described herein. In some embodiments, the method may include performing voxel-based tau quantification in the hippocampus. For clinical response validation, perform tau imaging analysis to track clinical response as described herein. In some embodiments, the clinical response validation may include performing a clinical response follow-up measurement. In some embodiments, the clinical response validation may include at least one of the following: Including: performing tremor quantification: or performing automated tremor quantification , where quantification of tremor is optionally by genotype. The literature may provide detailed information regarding tremor quantification, etc. Answer verification may include: voxel-based serotonin receptor ligand binding from images; Calculate the following:

[0049] The method 100 also includes determining a genotype based on at least one candidate gene biomarker for the condition. , which may include performing a phenotype prediction from the deep learning from the SNP model. There may be a step 150 for predicting a phenotype with learning. The prediction of the phenotype is a validation of the protocol of one or more identified biomarkers. In some embodiments, the prediction of the phenotype may be performed using positron emission tomography (PET) images. In some embodiments, the phenotype prediction may include quantitative phenotypic analysis using deep learning. The SNP model may predict voxel-based amyloid SUVR. Phenotype prediction using voxel-based DAT-SPECT quantification with deep learning SNP models In some embodiments, the prediction of the phenotype may be performed using a deep learning SNP model. In some embodiments, the phenotype can be predicted based on alpha-synuclein ligand binding. The measurements can predict local ligand binding with deep learning SNP models.

[0050] In some embodiments, the method also includes determining whether one or more biological pathways associated therewith are present. In view of this, the method may include analyzing one or more candidate biomarkers. This may include analyzing at least one Identifying at least one therapeutic target for a phenotypic state based on candidate gene biomarkers The therapeutic target may include determining a candidate biomarker that encodes for the therapeutic target. or a substrate with which the therapeutic target interacts in one or more biological pathways associated therewith. or other substances. In the method, at least one therapeutic target for the condition is at least one candidate gene biomarker. Thus, a therapeutic target is a known or novel therapeutic target that modulates a therapeutic target. may be selected for analysis of therapeutic agents (e.g., new chemical entities (NCEs)). In another example, a therapeutic target can be targeted for inhibition of biological activity. and / or may be targets for increased biological activity. In one embodiment, the identification of a therapeutic target is , step 120, 130, 140 or 150 or part of their sub-steps In any case, knowledge of the candidate biomarkers may be useful in identifying related biological pathways. The analysis may lead to the identification of one or more therapeutic targets. .

[0051] In some embodiments, the method also includes providing a report, whether paper or electronic. The generation of a report may include, but is not limited to, the generation of a report. The generation of a report may be performed in accordance with any method, such as the method 100 described herein. The report may include information obtained by any method. may be generated and / or distributed using a protocol or system for, or for use by, a physician; Can be edited for delivery as a report. Figures 7, 8, 9, 10, 11, 12, 14, 1 5 and / or 17-19, or similar data types or formats, The report may include result data such as determined metrics. The report may include one or more candidate biomarkers associated with the condition. The report may then include the validated and specifically identified biomarkers. The report may be provided to one or more subjects and to medical professionals or researchers working on the condition. may also include information regarding a treatment to be used to treat the condition. generating a report including at least one therapeutic target identified for the condition. With respect to the diagram, "end" may be the end of the analysis, which may result in a report may be generated (e.g., automatically or upon command by a protocol operator) In at least one embodiment, the method further comprises: and performing one or more steps of the method recursively to refine the resulting data, such as the number of samples. obtain.

[0052] In some embodiments, the method includes: In some aspects, the method may include steps or steps of obtaining an image of a phenotype of a subject. The image may include a precise depiction of the state at a given point in time. In an embodiment, the method comprises a quantitative phenotype with a representation greater than or equal to 85% or about 85%. In some embodiments, the method includes obtaining a phenotypic accuracy from an image of the subject's phenotype. In some embodiments, the method includes detecting a disease as a condition from the disease. In some embodiments, the method may include defining disease activity at each point on the spectrum. performing a standardized uptake value ratio (SUVR) analysis using the image of the phenotype of interest. obtain.

[0053] In some embodiments, the method 100 includes a step of performing quantitative image analysis of the phenotypic images. In some aspects, the method 100 may include specific method steps for: Calculate the voxel-based amyloid standardized uptake ratio (SUVR) from the images ( Step 210 in Figure 2); Voxel-based dopamine transporter single photon counting from images Calculating the Dispersion Tomography (DAT-SPECT) quantification (step 310 of FIG. 3 ); Calculating voxel-based α-synuclein ligand binding from images (step 4 in Figure 4) or calculating local serotonin receptor ligand binding from the images (Figure 5 step 510).

[0054] In some embodiments, the images used in the method are captured by a standard camera (e.g., a CD It can be any image from any device, including digital images from a computer (C, CMOS, etc.). However, in some embodiments, the images are generated using imaging techniques commonly used in the medical field. These may include positron emission tomography (PET), magnetic resonance imaging, Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and their combinations and Others may be included.

[0055] In some embodiments, the method 100 includes various protocols for performing clinical response validation. Such a protocol may include at least one of the following: One may include: Hippocampal masked voxel-based tau SU with genotype quantification To perform VR; quantification of hippocampal genotypes to assess voxel-based tau subtypes To perform memory tracing characterization using VR; voxel-based tau quantification within the hippocampus performing; performing tau imaging analysis to track clinical response; or The clinical response validation (step 140) may also include: To compare the sampled voxel-based tau SUVR by genotype (step 24 in Figure 2) 0); comparing tremor quantification by genotype (step 340 in FIG. 3 or step 340 in FIG. 4); or to calculate voxel-based serotonin receptor ligand binding (Figure 5 step 540).

[0056] As shown in FIG. 1, the method 100 may include: performing accurate quantitative phenotypic analysis; (Step 110); Genome-wide association study of quantitative trait loci (QTL GW and performing quantitative image analysis and quantitative genome analysis (step 120). Obtaining at least one candidate gene biomarker for a condition in a plurality of subjects (step Step 130); at least one candidate gene biomarker is to be validated. predicting clinical response to the candidate gene biomarkers (step 140); and , performing phenotype prediction using the deep learning SNP model (step 150).

[0057] FIG. 2 also provides a method 200 for identifying genetic biomarkers for a condition, which includes: Calculate voxel-based amyloid SUVR from PET and T1 MRI images. (Step 210); Genome-wide association analysis of quantitative trait loci (QTL G 2. Performing quantitative image analysis and quantitative genomic analysis (WAS) (step 220); obtaining at least one candidate gene biomarker for a condition in a plurality of subjects (condition); 230); hippocampal masked voxel-based tau SUVR compared by genotype and (step 240); the deep learning SNP model is used to calculate the voxel-based amyloid SU Predicting the VR (step 250).

[0058] FIG. 3 also provides a method 300 for identifying genetic biomarkers for a condition, which includes: May include: voxel-based DAT-SPECT from SPECT and T1 MRI images Calculating the quantification (step 310); genome-wide association analysis of quantitative trait loci (Q performing quantitative image analysis and quantitative genomics (TL GWAS) (step 320); Obtaining at least one candidate gene biomarker for the condition in the plurality of subjects from the analysis. (step 330); comparing the tremor quantification by genotype (step 340); and and predicting voxel-based DAT-SPECT quantification using deep learning SNP models (Step 350).

[0059] FIG. 4 also provides a method 400 for identifying genetic biomarkers for a condition, which includes: Voxel-based α-synuclein ligand binding from PET and T1 MRI images calculating the association of the quantified trait loci (step 410); 4. Performing quantitative image analysis and quantitative genomics (QTL GWAS) (step 420); The analysis identifies at least one candidate gene biomarker for the condition in a plurality of subjects. obtaining the tremor (step 430); comparing tremor quantification by genotype (step 4 40) Voxel-based α-synuclein ligand binding using deep learning SNP models (Step 450).

[0060] FIG. 5 also provides a method 500 for identifying genetic biomarkers for a condition, which includes: PET and T1 MRI images to measure local serotonin receptor ligand binding. calculating (step 510); 5. Performing quantitative image analysis and quantitative genomic analysis (WAS) (step 520); obtaining at least one candidate gene biomarker for the condition in the plurality of subjects; (Step 530); voxel-based serotonin concentration is calculated using the PET and T1 MRI images. calculating receptor ligand binding (step 540); and predicting local serotonin receptor ligand binding using (step 550).

[0061] The embodiments shown and described with respect to FIGS. 1-5 are by way of example only and are not intended to be limiting unless otherwise specified. An example would be a genetic biomarker for a condition. The present invention provides protocols, data and information for data analysis. However, the protocol can be used to detect other genetic biomarkers. It should be recognized that the method may be applied to other conditions or subtypes thereof to determine the It is.

[0062] In some embodiments, any of the methods further comprises the step of: The method may include analyzing a biological pathway with a biomarker. With respect to known biological entities of the target pathway, as well as known modulators of known biological entities Thus, biological entities that can be modulated by known modulators can be analyzed therapeutically. Therapeutic targets can be identified that can be modulated by known chemical entities, thereby These potential treatments may then be included as potential treatments for the condition. However, known biological entities may also be used in the treatment of the condition. and may be studied to determine new chemical entities (NCEs) that may be modulators thereof. The known biological entities may be identified through in silico or real-world evaluations or their Combinations may be studied.

[0063] In some embodiments, the condition is a syndrome, disease, disorder, etc. A syndrome is a phenotype that is and a set of medical signs and symptoms that may be indicative of a condition and that correlate with one another in a subject. A disease is a pathophysiological response to factors external or internal to the subject that can be manifested phenotypically. The disorder may result in disruption of the normal body structure and / or function of the subject.

[0064] Some examples of conditions described herein include Alzheimer's disease, Parkinson's disease, and However, other illnesses may be amenable to the techniques described herein. It can be used in conditions such as

[0065] FIG. 6 illustrates an embodiment of a system 600 for identifying genetic biomarkers for a condition. The system 600 is shown to include at least the following: a deep phenotyping unit; QTL GWAS analysis unit 6 downstream of deep phenotyping unit 610 20; and the Clinical Response Validation Unit 6 downstream of the QTL GWAS Analysis Unit 620. 30. These components may be used to carry out the methods described herein. The system 600 includes a gene expression analysis unit that may be downstream of the clinical response validation unit 630. The system 600 may further (optionally) include a clinical response validation unit 640. 30, and optionally downstream of gene expression unit 640, a pathway mapping unit The system 600 may further (optionally) include a clinical response verification unit 650. 630, and / or optionally downstream of a gene expression analysis unit 640, and / or or optionally downstream of the pathway mapping unit 650, further (optionally) phenotypic prediction The system may include a measurement unit 660.

[0066] FIG. 6 also shows the system 600 including a toolset system 670. The system 670 may include one or more units selected from the following: and / or trend analysis unit 672; statistical analysis unit 674; text mining validity Analysis Unit 676; New Chemical Substances (NCE) Prediction Unit 678; or Report Unit 680.

[0067] In some embodiments, the system 600 operates in accordance with one or more of the following: The deep phenotyping unit may include components configured in accordance with the disclosure of the specification. The step 610 may be configured to perform steps 110, 210, 310, 410 and 510. The QTL GWAS analysis unit 620 performs steps 120, 220, 320, and 420. and 520; the clinical response validation unit 630 may be configured to perform steps 14 The gene expression analysis unit can be configured to perform 0, 240, 340, 440 and 540. The unit 640 is a gene expression profiling system that may or may not be automated. The path mapping unit 650 may be configured to perform the filing 640. One or more biological pathways of genes identified by gene expression analysis unit 640. The phenotype prediction unit 660 may be configured to map the phenotype of the individual to the phenotype of the individual. to predict the phenotype identified by the deep phenotyping unit 610 or to generate new The patterns and / or methods may be configured to predict new phenotypes for new investigations of novel conditions. Or the trend analysis unit 672 may include a deep phenotyping unit 610, a clinical response validation unit 630, together with the gene expression analysis unit 640, to determine phenotypic patterns or The statistical analysis unit 674 may be configured to analyze any data from the method. The text mining validation analysis may be configured to perform statistical analysis of the data or metrics. The analysis unit 676 determines the appropriateness of a therapeutic target by text mining. and / or determining chemical modulators of biological entities of biological pathways related to therapeutic targets. A new chemical entity (NCE) prediction unit 678 can be configured to predict therapeutic targets. The reporting unit 680 may design or generate an NCE that can adjust the The software can be configured to generate reports based on protocols included in the references. The biological modulator or NCE may also be replaced by a biologically active agent. Potential drugs include biological agents such as small molecules, peptides, biopharmaceuticals, antibodies, nucleic acids, and siRNAs. The chemical may be any chemical, whether natural or synthetic.

[0068] In some embodiments, the components of the system 600 include a computing system The computer may be a computer or computer module that can run on the system. The components are executed by a processor (e.g., a microprocessor) of a computer. Then, the method and method steps described herein for any protocol can be performed. The program may include computer executable code that can cause the program to execute a program.

[0069] FIG. 13 illustrates a DeepQnt processing pipeline that can be used in the methods described herein. As shown, the processing pipeline 1300 has the individual steps shown: This may be achieved by a computing system (e.g., systems 600, 61 0, 660) and as described in more detail in Example 4 - Methods. The steps can be illustrated in FIG. 13. The methods provided herein are for deep learning and artificial intelligence. In some examples, the present invention may be implemented on a computing system, such as a The method includes obtaining data and processing the data to obtain a recommendation for a treatment protocol. The recommended treatment protocol may then be based on the parameters of the treatment protocol. i.e., without computational generation of treatment protocols, Aspects of the protocol cannot be carried out unless instructed to do so. Obtaining instructions for a class, specific medication, or combination of medications to implement a treatment protocol In some cases, it may be essential to EXAMPLES

[0070] Example 1 The first integrated quantitative measure that accurately reflects disease at a given time point (cross-sectionally) in a single measurement Targeted “deep” Alzheimer’s disease (AD) imaging phenotypes 110 (Fig. 1), 210 (Fig. 2) ), 700 (Figure 7): Agreement between automated voxel-based amyloid PET quantification and ATN classification Reference is now made to FIG. 7. FIG. 7 shows the new NIA-AA “ATN” criteria (e.g., ck et al., Alzheimer's and Dementia 2018) Quantitative "deep" (e.g., disease-defining metric) phenotypes with high (> / = 85%) accuracy (e.g., correct phenotype). Phenotyping can be done, for example, by analyzing voxels from amyloid PET and T1 MRI scans. The newly proposed "ATN" classification is based on amyloid, Using dichotomized biomarkers of tau and neurodegeneration, eight possible biomarker sets were "T" is motivated by AD neuropathology, but CSF samples In addition, in patients with MCI, "N" is the same as in those with AD. This is thought to be more informative in terms of clinical translation to , Burnham et al., AAIC 2017). The purpose of the analysis shown in Figure 7 is to Voxel-Based Amyloid PET Quantification Software Validated in More Advanced Disease Ability of the study to identify preclinical and prodromal AD groups established by ATN status The purpose of this study was to evaluate the following:

[0071] Example 1 - Method The method included 229 non-demented subjects (not used for training): 86 1 healthy controls (CTR), 143 MCI patients. All subjects had complete The patient had an ATN profile. 18 F]florbetapir PET Amyloid positivity was assessed using syngo.PET, a region-based quantitative method approved for clinical use. "T": CSF p-tau status was established using SPAP amyloid plaques. , established with the new Elecsys assay. "N": Mean left / right hippocampal volumes were Calculated with rapid MRI quantification previously validated against the C / ADNI harmonized protocol. Voxel-based SUVR from corresponding PET / T1 MRI scan pairs was performed using the ATN projection. Identifying preclinical or combined preclinical / prodromal groups based on profiling Evaluated for ability (+,-, "x" indicates + or -).

[0072] Example 1 - Result: Preclinical AD (defined as A+T+Nx, n = 29) versus A-TxNx / A+T-Nx (n = 57) CTR subjects to distinguish the area under the receiver operating characteristic curve (AU C) was 0.917. Graph 710 (A+T+Nx vs. A-TxNx / A+T-Nx In the (preclinical AD vs. others) group, the lines indicate sensitivity / specificity / precision (sens / spec / acc)%, which was 86 / 86 / 86 with a cutoff of 1.23. / prodromal AD (defined as A+T+Nx, n = 86) versus A-TxNx / A+T-Nx (n = 143) The AUC of Graph 720 for CTR / MCI subjects was 0.918. Graph 72 0 (A+T+Nx vs. A-TxNx / A+T-Nx (preclinical / prodromal AD vs. others)) The lines indicate the sensitivity / specificity / accuracy percentages, which are 81 / 91 / 10 at a cutoff of 1.23. The result was 87. Graph 730 is A+T+ / A+N+ (n=88) and A-TxNx / A+ The AUC for discriminating between TN- (n=141) CTR / MCI subjects was 0.928. Graph 730 (A+T+ / A+N+ vs. A-TxNx / A+TN- (Preclinical / Precursor A For D / A+T-N+ vs. others), the lines indicate Sensitivity / Specificity / Accuracy %, which is The cutoff was 1.23 and the results were 82 / 92 / 88. For graph 715, p=9.6×1 0 -14 In graph 725, p=1.1×10 -35 It was. Graph 735 So, p=6.9x10 -39 In the graphs 710, 720 and 730, the X-axis is 1-specificity and the Y-axis is sensitivity.

[0073] Therefore, as shown in FIG. 7, the voxel-based amyloid PET determination in graph 710 The amount was measured by the Reference Clinical Area-Based Quantitation Method (SPAP), the Elecsys assay for p-tau, and rapid hippocampal volumetry, as determined by preclinical established ATN classification and grading. Good agreement with the Raf720 preclinical / prodromal disease biomarker profile (A+T+Nx) The disease definition for preclinical / MCI stages was previously considered in the NIA-AA framework. Based on A+T+ or A+N+ as reported (stage 2 / 3, MCI-AD- high), and the accuracy of Graph 730 was equally high.

[0074] Please refer to FIG. 6. FIG. 6 shows the main components as core modules 610 to 660. and toolset library 67 used with core modules 610 to 660 Data of an exemplary intelligent system for therapeutic target identification in drug discovery along the FIG. 1 shows a data flow diagram; a skilled artisan can easily understand the preclinical disease defined by the new ATN classification. A single integrated quantitative PET measurement with unit 610 (Figure 6) for accurate detection of disease The use of 110 (FIG. 1) or 210 (FIG. 2) is in accordance with the important aspects detailed in this disclosure in the following respects: will understand that this is an important consideration: novel Genetic drivers of disease in neurodegenerative and other CNS disorders for therapeutic target identification An efficient, computer-implemented method 100 (FIG. 1) and system for determining In particular, in the Alzheimer's disease (AD) treatment system (e.g., FIG. 6), The present invention provides a system 600 (FIG. 6) for identifying genetic drivers of disease in Merck's disease. In accordance with the above, an efficient computer-implemented method 200 (FIG. 2) is provided.

[0075] The present disclosure provides a method for determining whether a first accurate quantitative phenotype (e.g., step 11) is ... 0, step 210, unit 610), e.g., amyloid PET and T1 M After obtaining the first voxel-based SUVR210,700 from the RI scan, the second scan Steps 140 (Fig. 1) and 240 (Fig. 2) are combined with unit 630 (Fig. 6). The technology to be introduced will be explained below.

[0076] In one embodiment, the present invention then provides a method for determining the identity of a target genotype, as further described below. Imaging or non-imaging quantitative measurements of clinical responses (e.g., step 140, step 24 0, see unit 630); in one particular embodiment, the measurement The value is the voxel-based tau PET quantification in the hippocampus in step 240 and step 24 Quantification of 0 will be, at least in part, performed using machine learning / AI-based image analysis operations to determine the The clinical response is evaluated at step 140.

[0077] In the above-described exemplary embodiment of the method 200, the first image table in step 210 The proven high accuracy of the current Data 700 is, in principle, based on machine learning / AI steps. The alternative metric of step 110 that closely correlates with the voxel-based SUVR of step 210 This can be achieved by

[0078] Similarly, and in the context of the present disclosure, the voxels in the hippocampus in step 240 The memory tracking properties of the based tau PET quantification have, in principle, already been demonstrated here. (see details in Example 2 below), using said machine learning based image analysis operations, and / or The method involves the selection of a specific voxel set, such as the CA1 region of the hippocampus, combined with the selection of a specific voxel set. This can be achieved using a simple mask.

[0079] Another exemplary embodiment of the present disclosure is outlined in FIGS. Figure 3 shows a computational approach to identify genetic drivers of disease in Parkinson's disease. 3 is a flowchart of a method 300 implemented in the computer, in which DAT-SPECT and and the voxel-based DAT- Automated in 340 steps for SPECT deep imaging phenotyping and clinical response assessment Alternatively or additionally, the automated tremor quantification from step 340 is used. The quantification of tremor obtained may be based on machine learning / AI.

[0080] Similarly, Figure 4 shows a cohort study to identify genetic drivers of disease in Parkinson's disease. 4 is an example of a flowchart of a computer-implemented method 400, comprising: The voxel base in step 410 is calculated from the PET and MR imaging. Automated in step 440 for deep imaging phenotyping of tumors and clinical response assessment. Alternatively or additionally, the automated tremor quantification from step 440 is used. Quantification of tremor may be based on machine learning / AI.

[0081] In yet another example, Figure 5 shows local serotonin receptor ligands from PET and T1 MRI. a first deep imaging phenotype calculated from the bond combination in step 510; and Step 540 metrics from PET and T1 MRI for clinical response assessment Using a second voxel-based serotonin receptor ligand binding assay, we have determined whether major depressive disorder (MDD) is associated with A computer-implemented method for identifying genetic drivers of disease in DD 5 is an example of a flowchart for 500. Alternatively or additionally, the metric from step 540 may be The metrics (e.g., voxel metrics) can be based on machine learning / AI.

[0082] Example 2 Predicting clinical response by computing hippocampal masked voxel-based tau SUVR : Please refer to Figure 8. Figure 8 shows a voxel-based analysis of the hippocampus 824 to assess clinical response. Tau PET quantification (see, e.g., step 140, step 240, unit 630) 8 shows preliminary results of graphs 810 and 820 in 30 subjects. This may be related to either tau PET quantification or amyloid PET quantification. 820 than reported by other researchers. The correlation was 0.01 (r=-0.60, p<0.001). 3, n = 30, non-converters are indicated by open circles, and 12 early AD subjects were eAD are indicated by black circles (8 / 12 subjects, including 4 subjects with mild AD, had conversion events). Tau scan obtained before or within 1 year of the test. For Graph 820, r = -0.60 (p <0.001) and y = -5.93x + 34.2 (n = 30).

[0083] Those skilled in the art will appreciate that tau PET quantification within the hippocampus to date has overlooked the true boundaries of the hippocampus. This is partially hindered by the lack of co-registered MRI segmentation, resulting in For example, we will understand that the antibody is contaminated by off-target binding in the choroid plexus. therefore, the hippocampus correlates well with cognitive measures such as the MMSE (the gold standard of clinical response). Since no evidence has been shown to be relevant to the measurement, this has been considered an inappropriate site for measurement. According to one aspect of the embodiment, the technique avoids such overshooting and optimizes clinical response. Incorporating follow-up tau imaging measurements may solve this problem.

[0084] Predicting and measuring response to tau-directed therapy in early Alzheimer's disease (AD) It has the specific goal of accurately reflecting disease activity at the earliest stages and locations of neurodegeneration and providing clinical There is great interest in novel approaches that better address floor outcomes. Such studies have been carried out using a general or composite region of interest and / or z-score based approach. Initial attempts to derive such measures have met with mixed success so far, and / or This is hindered by the additional need to incorporate prior knowledge of amyloid-positive status. Ntecorvo et al. (Brain 2017) reported that 18 F]Flortaucipir They reported promising results from a PET cohort analysis. Notably, the hippocampal subregions (s SUVR calculated from the ubregions was more effective at group discrimination than the composite neocortical SUVR. The purpose of the analysis shown in FIG. 8 is to determine whether the data 800 is a voxel-based (Hippocampal masked) tau quantification (e.g., step 140, step 240, unit 630) and memory 822 (e.g., baseline MMSE), and Distinguishing data in graph 810 for MCI non-converted subjects 812 from AD subjects 814 The objective of the study was to demonstrate its ability to

[0085] Example 2 - Method: Involved 30 subjects with MCI or AD816 (dataset not used in Example 1) They were divided into two groups: 18 non-MCI converts (MCI follow-up 4-5 years) and and 12 subjects with mainly early AD (8 / 12 subjects had a conversion event from MCI to AD). PET scans were performed within 1 year before or after the trial; furthermore, limited data were available at the time of abstract submission. (4 / 12 subjects with mild AD were included because they had been previously treated with schizophrenia.) 18 F]F Lortaucipir PET, T1 MRI scan and MMSE score (PET ) were also obtained. Hippocampal masks were obtained using the previously described EADC / ADNI Harmonized Protocol (HarP ) multi-atlas, GPU-accelerated, machine learning-based The hippocampal segmentation was derived from the T1 MRI. The VR maps (75–105 min, cerebellum reference) were subjected to partial volume correction and were compared with the MNI space. The best voxels in the hippocampus that distinguished the non-converted and early AD groups were was determined based on voxel-wise t-score cluster analysis. Voxel-based SUV R is a leave-one-out analysis. The correlation between hippocampal voxel-based SUVR and MMSE was , assessed by Pearson's correlation.

[0086] Example 2 - Result: For the 810 group classification, the area under the receiver operating characteristic curve (AUC) was 0.79. (Two-tailed t-test for group differences: p=0.01 3) Hippocampal voxel-based SUVR824 was significantly correlated with MMSE score822. (r=-0.60, p<0.001).

[0087] The voxel-based approach significantly improved local hippocampal SUVR (AUC = 0.60, r = -0. 49) and hippocampal volume (AUC = 0.73, r = 0.27).

[0088] [ 18 F]Flortaucipir (18F-AV-1451) SUVR and Basera The correlation with the MMSE has been reported by other researchers as ~r=- for both global and regional measures. It has been shown previously to be on the order of 0.4, but the approach in Example 2 described here Chi identified a subgroup of voxels within the hippocampus that correlated better with MMSE, and According to one aspect of the embodiment, by incorporating tau imaging measurements that track clinical response well and resolve the problem (see, for example, step 140, step 240, unit 630). The potential effect of choroid plexus uptake was observed in the hippocampal pleura distal to the PVC and part of the choroid plexus. Furthermore, from the perspective of disease biology, the hippocampus Hyperactivity of β-lactamase inhibitors (CAIs) has been further investigated as a driver of disease and has been linked to tau deposition in prodromal AD. Therefore, much less human data is required to identify novel therapeutic targets. Efficiency to uncover genetic drivers of disease in neurodegenerative and other CNS disorders More specifically, in the exemplary computer-implemented method 100 and system 600, The much smaller set of human data is needed to identify novel therapeutic targets for Alzheimer's disease. An efficient, computational approach to reveal genetic drivers of disease in humans In the method 200 and system 600 described above, this is an important consideration in the context of the present disclosure.

[0089] Example 3 The first integrated quantitative "deep" Alzheimer's disease (AD) imaging phenotype QTL GWAS analysis: Those skilled in the art can use, for example, PLINK to perform QTL GWAS analysis (e.g., step 120 , step 220, unit 620), in combination with the target specific method as shown in Example 1. High (> / =85%) accuracy (according to the new NIA-AA "ATN" standard) quantification Targeted "deep" phenotyping (see, e.g., step 110, step 210, unit 610) We understand that using genomic DNA fragments is an important step in identifying potentially novel SNPs. Unit 620 will explore other QTL GWAS techniques, such as random forests. Machine learning based techniques may be used. , see Step 210, Unit 610) to identify more advanced disease (AD dementia) It has been previously demonstrated that the quantitative phenotype can be detected with 100% high accuracy. (Step 110, Step 210, Unit 610) across the disease spectrum Reflects disease activity and identifies new therapeutic targets from much smaller human data ,An ,efficient ,research ,to ,uncover ,the ,genetic ,drivers ,of ,Alzheimer's ,disease. In the disclosed computer-implemented method 200 and system 600, This makes its use more suitable in conjunction with TL GWAS.

[0090] In one aspect of an embodiment, genotyping is performed on all n=334 Discovery samples were run using Illumina Omni2.5 microarrays. Ta.

[0091] Table 1 shows the quantitative phenotypes (e.g., step 110, step 2) according to examples of the present disclosure. 10, unit 610) for the QTL GWAS120, 220, and 620 The list of 33 disease-associated SNPs detected as a result is shown below. ID does not reflect rank here), chromosome (Chr), SNP (accession variant identification number, also called rsid), and variant allele / base ( A1 indicates the minor allele, and A2 indicates the major allele) are shown in Table 1. A rele can be considered a SNP for the purposes of determining whether a SNP indicates a low or high probability of a disease. The major allele provides an indication opposite to that provided by the minor allele. It is a common allele.

[0092] [Table 1]

[0093] The above detailed steps 110 to 150, 21 in the intelligent system 600 0 to 250 and units 610, 620, 630, and the computer disclosed herein. The methods 100 and 200 implemented in the data acquisition unit 640 generate gene expression profiles. The automated steps for performing the filtering and the path mapping unit 650 are Further target validity analysis in units 672, 674, 676, and 680, including According to one embodiment, the set of candidate SNPs / genes as listed in Table 1 may be used for further analysis. The SNP / gene (e.g., step 130, step 140) serves as an additional validation step. 230) from a deep learning model (e.g., step 150, step 250, The first image representation (e.g., step 110, step 120, see unit 660) is provided with The method can be further developed to accurately predict the eigenvalue (see step 210, unit 610).

[0094] Example 4 Deep learning-based simulation in intelligent systems for therapeutic target identification in drug discovery Stem (DeepQnt) predicts phenotypes from much less genotype data, and ,Polygenic Hazard Score (PHS, Desika Direct performance comparison with Sigma et al., PLoS Medicine 2017): Please refer to FIG. 14. FIG. 14 shows the phenotype (PETQ SUVR) (e.g., step 11 0, step 210, unit 610), which is a diagram showing the therapeutic The disclosed computer-implemented method is implemented in an intelligent system 600 for therapeutic target identification. A deep learning vector in a computer-implemented method (see, for example, method 100 or method 200). System of the source (DeepQnt) (e.g. step 150, step 250, unit PETQ SUVR1412 predictions with amyloid positive Subjects who underwent PET scans (assessed with SPAP, an FDA-approved method) were P+ (triangle symbol). Prediction 1414 (e.g., predicted SUVR) is, e.g. For example, to obtain PETQ SUVR1412, step 110 or step 110 is performed in unit 610. The correlation coefficient for the original quantitative phenotype obtained by the 210-fold increase was Pearson's r = 0.411. Significant correlations can be achieved.

[0095] Please refer to FIG. 15. FIG. 15 illustrates, for example, the disclosed computer-implemented method 1. 00 or unit 660 in method 200, and for therapeutic target identification in drug discovery. By using the intelligent system 600 of step 150 or step 250 A, the performance of an exemplary phenotype prediction (DeepQnt); SPAPpos is Amino Lloyd-positive PET scan; triangle symbol indicates conversion from MCI to AD; long-term (> / Mild cognitive impairment (MCI) to AD in a dataset with follow-up of 10 years (=5 years) This shows the NC / C conversion.

[0096] In the graph 1510, a prediction 1514 (e.g., predicted SUVR) is In addition to classifying groups by sexual status (e.g., SPAP positive), It is also possible to classify converters C (triangle symbol) from non-converters NC (square symbol) The respective receiver operating characteristic (ROC) curves are shown in Graph 1520 (C vs. NC). The area under the curve (AUC = 0.757) and graph 1530 (A of amyloid positive detection UC 0.724).

[0097] These performance results are illustrated by examples of powerful computational techniques for Alzheimer's disease (AD). The technique allows for therapeutic target discovery in just a few hundred samples and polygenic sequencing across thousands of subjects. Other gene expression data, such as the Personalized Hazard Score (PHS) (detailed data are shown in Figures 17-18), The results showed that the APOE4 carrier status could be predicted by the same method as the conventional method. compared with only statis (sensitivity 76%, specificity 67%, accuracy 72%; data not shown). Improved ability to detect early AD (detection of conversion from MCI to AD; sensitivity 91%, isomerism 62%, accuracy 80%; Figure 19).

[0098] Example 4 - Method: A total of 334 subjects were included in the training: 128 healthy controls (CTRs); 59 MCI and 47 AD. Testing included separate data sets of 54 MCI subjects. The sample included 33 converters (to AD) and non-converters. (PET 1-3 years before conversion) and 21 non-converters (MCI follow-up 5-7 years) .handle[ 18 F]florbetapir PET, T1 MRI, Illumin a Omni2.5M genotyping data, reference clinical domain-based quantification method (SPAP), Used to establish amyloid positive (SPAP+) status and polygenic haematopoietic only a subset of 54 MCI cases (52) had a PHS score. 47 had access to a SPAP; 45 had both a PHS and a SPAP. The first voxel-based SUVR experiment to obtain PETQ SUVR1412 The imaging phenotype (e.g., step 110, step 210, unit 610) may be, e.g., was calculated for each PET / MRI scan pair as detailed in 1.

[0099] Voxel-based tau PET quantification within the hippocampus (e.g., step 140, step 24 0, see Unit 630) The mice were further used to verify the clinical response, as detailed in Example 2. Here, quantification was performed at least in part using machine learning / AI-based image analysis procedures. there was.

[0100] Models without covariates and with APOE4 carrier status as a covariate were included. Using the voxel-based SUVR phenotype (e.g., step 110, step 210, Unit 610, see PETQ SUVR1412) associated with candidate SNPs (e.g. Step 130, Step 230) to detect PLINKv1.9 beta (linear Dominant model, MAF>0.1) (e.g., step 120, step 220, unit 6 20) was used. The artificial neural network (ANN) was the first SUVR phenotype of the strain (e.g., step 110, step 210, unit 610, PE A set of 32 SNPs (Table 1, SN The dataset was trained using GPU-accelerated TensorFlow (see P ids 1-32). v1.1.0) and selected parameters (batch size=4,keep pro ability=1.0,initializer=Xavier,optimize r=Adam,learning rate=0.001,layers=[32-22 -1], iterations=350). 14 was calculated for each subject using the ANN model. In the method, the ANN is a subset of SNP ids 1 to 32 in Table 1 and optionally a subset of SNP ids 1 to 32 in Table 1. The first set of SNPs was further trained using genotype data containing only SNP id 33. It will be appreciated that the ANN can predict the voxel-based SUVR phenotype of a subject. For example, it can be implemented using toolkits such as Caffe, Pytorch, or MATLAB. and may be compiled as an executable or a C / C++ shared library. stomach.

[0101] Please refer to Figure 13. Figure 13 shows an intelligent system for identifying therapeutic targets in drug discovery. An exemplary phenotype prediction (DeepQnt) in system 600 (e.g., step 150, step Major components and data flows in the / Processing pipeline (steps 1-8); Set of candidate SNPs / genes ( For example, see steps 130 and 230) to perform deep sequencing of the discovered SNPs / genes. A multi-layer learning model is used to generate the original imaging phenotype (e.g., step 110, step 2 10, Unit 610, PETQ SUVR1412) can be constructed to predict , which can serve as an additional verification step.

[0102] More specifically, in one embodiment, the pseudocode for the discrete steps of FIG. Is: 1. Read input file (VCF / BCF) header, check sample, tabix Check files and update settings; 2. Raw header, sample number, sequence name, ##reference and ##file Extract format; 3. Key / values ​​for each SNP, including genotype, ploidy, and #occurrences Create a dictionary of; 4. Read each line of the input file and do the following: a.Compare with SNP dictionary key to see if there is a match; b. Determine the type of match (rs+bp, rs only, bp only); c. Extract REF characters, ALT characters, genotypes and character codes; d. Based on the genotype, select the appropriate tensorflow model code (1, 2 or 3). assign; e. Add the extracted information and model codes to the SNP dictionary; 5. Output the SNP information to a log file and verify that all required SNPs are present. acknowledge; 6. Generate a feature vector from the SNP genotype data to input to the tensorflow model. Generate tor; 7. Recode genotypes into non-carrier / carrier and run them using the tensorflow DNN model Run Dell; and 8. Convert the output from the tensorflow model into formatted SUVR output values , output to the terminal.

[0103] Example 5 Disclosure related to Alzheimer's disease LPCAT2 candidate gene and SNP rs4402 561 discoveries.

[0104] See Figure 9. Figure 9 reveals genetic drivers of disease in Alzheimer's disease The report generated by the system 600 includes a unit 680 for FIG. 1 shows an example, here as an example, of a candidate LPCAT2 gene / protein; As will be appreciated by those of skill in the art, the protected candidate SNPs (e.g., steps 130, 23 0) Graph 920 suggestions for rs4402561 (see Table 1, SNP id33) A potential association is detected by the first accurate quantitative phenotype (PETQ) (e.g., step 11 0, step 210, unit 610, PETQ SUVR1412), and graph 93 The step-down pattern at 0 (e.g., unit 672, unit 674) Confirmed by 2 clinical response follow-up measures (training p=0.054) (PETQ TAU) (e.g., step 140, step 240, unit 630). Graph 910 shows the 97 / 162 / 75 (The data on the left is 2 / 2, the data in the center is 1 / 2, and the data on the right is 1 / 1. ) rs4402561 data. Graph 920 shows n=97 / 237 (left Data on the right are non-carriers, data on the left are carriers), p=2.72e-5, rs4402 561 data are shown. Bonferroni significance level = 0.05 / 1589548 = 3 .15e-8. Graph 930 shows n=6 / 12 (data on the left is non-carrier, data on the right is carrier), p=0.054 for rs4402561.

[0105] In Table 1, in id33, QTL GWAS (e.g., step 120, step 220, unit 620) revealed by the rs4402561 SNP (e.g. , step 130, step 230) the base corresponding to the protective feature is A1 of the minor allele Those skilled in the art will appreciate that in this QTL GWAS example, A1 is a minor allele, but Regardless, the minor allele (MA) designation is used to identify population minor alleles used in association studies. The actual base substitution depends on the genotyping frequency (MAF) used during the analysis. understand that documents may need to be translated depending on the methodology and coding / reporting standards. For example, in the case of rs4402561, the salts shown as relevant protection The group is C, but is G when reported in the reverse strand orientation.

[0106] Please refer to Figure 10. Figure 10 reveals genetic drivers of disease in Alzheimer's disease. In this case, the report 1000 generated by the system 600, 680 , which shows, by way of example, a candidate LPCAT2 gene / protein; Thus, blood expression profiling graphs 1010, 1020 are likely to be regulated transcripts. Highly significant step-up patterns of associated LPCAT2 probes 1010, 672 , 674. Graphs 1010 and 1020 are LPCAT2-11725418 , N=49. In graph 1010, the data on the left is 22, and the data in the center is 12. In graph 1020, the data on the left is non-carrier and the data on the right is 11. The data is the carrier.

[0107] See Figure 11. Figure 11 reveals genetic drivers of disease in Alzheimer's disease. In order to achieve this, the reports generated by the system 600, which includes the units 680, etc. 1100, which is a diagram showing, by way of example, the candidate LPCAT2 gene / protein. ; one skilled in the art will appreciate that brain bank gene expression profiling 1120 may be used to , young controls (yCTR), age-matched old controls (oCTR) and Alzheimer's disease A profile 1120 of the LPCAT2 probe (e.g., unit 672, see unit 674) The data on the left graph is LPCAT-227889-hippocampus, N=62 (left data The data in the right is yCTR, the data in the middle is oCTR, and the data on the right is AD). The rough data is LPCAT2-227889-superior frontal gyrus al), N=69 data (left data is yCTR, center data is oCTR, right data is Data are A-D). Bonferroni significance level = 0.05 / 4 = 0.0125.

[0108] Please refer to Figure 12. Figure 12 reveals genetic drivers of disease in Alzheimer's disease. The report generated by the system 600, which includes a unit 680, etc. 1200, which shows examples of candidate LPCAT2 genes / proteins. Those skilled in the art will appreciate that the rs4402561 minor allele carrier status may be involved in the inflammatory cytokine production and / or The blood expression profiling 1210 of the enzyme IL6 is This indicates a trend step-down pattern as shown in the figure (see unit 672, unit 674). , it will be understood that protection is being described. Data is IL6-11746463, N=4 9 and p=0.031 (data on the left are non-carriers; data on the right are carriers). ). The Bonferroni significance level is 0.05 / 2 = 0.025.

[0109] In the above Example 5, the Alzheimer's disease LPCAT2 candidate gene and associated SNP rs Discovery of 4402561 identifies putative therapeutic targets upstream / downstream pathways using unit 650 Target validity analysis by text mining using mapping and unit 676 The downstream molecular pathway of LPCAT is further demonstrated by the expression of LPCAT in microglial cells. The small molecule selective LPCAT2 inhibitor (TSI) -01) was discovered in unit 676, and their respective SMILES codes are: Is: O=C(C(Cl)=C1Cl)N(C2=CC=C(C(OC(C)C)=O)C=C 2) C1=O.

[0110] Starting from a known molecular structure, tools such as SwissTargetPrediction Using this library, unit 678 enables structure-based drug design. For example, Atomwise AtomNet or other AI-based investigative Discover potential new chemical entities (NCEs) in silico using our in silico drug discovery tool For this purpose, the putative target of LPCAT2 itself can be used.

[0111] The present disclosure is intended as an illustration of various aspects, and the specific embodiments described herein are intended as examples. The present invention should not be limited to the embodiments described herein. Many modifications may be made without departing from the spirit and scope of the present invention. In addition to those enumerated herein, other variations and modifications are possible within the scope of the present disclosure. Functionally equivalent methods and apparatus are possible from the above description. For purposes of illustrating particular embodiments only and are not intended to be limiting. do not have.

[0112] In one embodiment, the method may include aspects performed on a computing system. To this end, the computing system includes a computer implementation for carrying out the method. The computer-executable instructions may include a memory device having computer-executable instructions. A computer containing one or more algorithms for carrying out any of the methods described in the document. The present invention may be part of a computer program product.

[0113] In one embodiment, any of the operations, processes or methods described herein are implemented using a computer. A computer-readable program stored on a data-readable medium and executable by one or more processors. The computer readable instructions may be executed or may be caused to be executed in response to execution of the computer readable instructions. Desktop computing systems, portable computing systems , tablet computing systems, handheld computing systems and and / or any other computing device. The method may be executed by a processor in a range of computing systems. The medium is not transitory. The computer readable medium is A physical medium on which computer-readable instructions are stored so that the instructions can be physically read by a be.

[0114] The present invention relates to a method for the production of a medicament for a medical device, a method for manufacturing a medical device, and / or a medical device that can affect the processes and / or systems described herein and / or other technologies. There are various media by which the information can be transmitted (e.g., hardware, software, and / or firmware). software), the preferred medium is the process and / or system and / or other technology deployed For example, an implementer may decide that speed and accuracy are paramount. In this case, the implementer may choose a primarily hardware and / or firmware medium; If performance is paramount, implementers may choose to implement primarily software; or, As a matter of law, an implementer may A combination of the above may be selected.

[0115] The various operations described herein may be implemented using a wide range of hardware, software, and firmware. or substantially any combination thereof, individually and / or collectively. In one embodiment, some portions of the subject matter described herein can be integrated into application specific integrated circuits. Circuits (ASIC), Field Programmable Gate Arrays (FPGA), Digital Signals It may be implemented via a digital signal processor (DSP) or other integrated format. Some aspects of the disclosed embodiments may be implemented in whole or in part by one or more components. As one or more computer programs running on a computer (e.g., one or more as one or more programs running on multiple computer systems), or as one or more programs running on multiple processors (e.g., one or more as one or more programs running on multiple microprocessors), firmware or substantially any combination thereof. and, in light of this disclosure, designing circuitry and / or software and / or It is possible to write code for the device or firmware. The subject mechanisms may be distributed as program products in a variety of formats, including those described herein. Exemplary embodiments of the subject matter relate to the actual signal-bearing media used to effect the distribution. This rule applies regardless of the specific type of signal transmission medium. Examples of physical signal transmission media include: But not limited to: Floppy disk, Hard Disk Drive (HDD), Compact discs (CDs), digital versatile discs (DVDs), digital tapes, Recordable media such as computer memory or other media that are not for temporary or transmission Other Physical Media. Examples of physical media having computer readable instructions include digital and / or analog Log communication media (e.g., fiber optic cables, wave guides, wired communication links, wireless communication links) It does not include transitory or transmission-type media such as data transfer, storage, or internet.

[0116] The devices and / or processes described herein may then be used by engineers to Using a ring technique, such devices and / or processes can be integrated into a data processing system. That is, the devices and / or processes described herein are generally At least a portion of the can be integrated into a data processing system through a reasonable amount of experimentation. A typical data processing system includes a system unit housing, a video display device, and a devices, memories such as volatile and non-volatile memories, microprocessors and digital signal processors processor, a computing entity such as an operating system, Graphical user interface, application programs, touchpad one or more interaction devices, such as a keyboard or screen, and / or a feedback loop and A control system including a control motor (e.g., feedback for sensing position and / or velocity) for moving and / or adjusting components and / or quantities A typical data processing system includes a data computing / communication such as are commonly found in signal and / or network computing / communication systems; It may be implemented using any suitable commercially available components.

[0117] The subject matter described herein may be included in or connected to different other components. The architecture shown in this way may be a single These are merely illustrative examples, and in fact many other architectures may be implemented which achieve the same functionality. In a conceptual sense, the arrangement of components to achieve the same functionality is a function of the required functionality. Therefore, to achieve a specific function, Any two components that are combined here will be Regardless of the components, they are considered to be "linked" to each other so that a desired function is achieved. Similarly, any two components so related can be related to each other. "Operably connected" or "operably coupled" is deemed to achieve a desired function. And any two components so associated can "move" with each other. "Operably coupled" may be considered to achieve a desired function. Examples include physically connectable and / or physically interacting components and and / or wirelessly interactable and / or wirelessly interacting components components and / or components that logically interact and / or can logically interact Components include, but are not limited to:

[0118] FIG. 16 illustrates some implementations for carrying out the methods (or parts thereof) described herein. An exemplary computing device 1600 that may be configured in the form of In a very basic configuration 1602, a computing device 16 16. The system 1600 generally includes one or more processors 1604 and a system memory 1606. The memory bus 1608 is used for communication between the processor 1604 and the system memory 1606. It can be used.

[0119] Depending on the desired configuration, the processor 1604 may be a microprocessor (μP), Controller (μC), Digital Signal Processor (DSP) or any combination thereof The processor 1604 may be of any type, including but not limited to a combination of One or more levels of cache, such as a level 1 cache 1610 and a level 2 cache 1612 An example processor may include a cache for a processor 1614 and a register 1616. The processor core 1614 is equipped with an arithmetic logic unit (ALU), a floating point unit (FPU) , digital signal processing cores (DSP cores), or any combination thereof. An exemplary memory controller 1618 may be used in conjunction with the processor 1604. In some implementations, the memory controller 1618 may be It may be a part.

[0120] Depending on the desired configuration, system memory 1606 may be volatile memory (such as RAM), non-volatile memory (such as EEPROM), or a combination of both. non-volatile memory (ROM, flash memory, etc.) or any combination thereof, The system memory 1606 may be of any type, including, but not limited to, the operating system. operating system 1620, one or more applications 1622 and programs The application 1622 may include data 1624 related to the methods described herein. A decision application configured to perform the operations described herein, including the operations described above. The determination application 1626 may include a pressure, flow rate, and / or or temperature and adjust the system to change the pressure, flow rate and / or temperature. Changes may be decided upon.

[0121] The computing device 1600 may include additional features or functionality as well as a basic configuration 160 2 and any additional interfaces that facilitate communication between the devices and interfaces required. For example, the bus / interface controller 1630 may be used , the basic configuration 1602 via a storage interface bus 1634 and one or more The data storage device 1632 may facilitate communication between the data storage device 1632 and the Removable storage 1636, non-removable storage 1638, or a combination thereof Examples of removable and non-removable storage devices are given below. Examples include flexible disk drives and hard disk drives (HDDs). Magnetic disk drive, compact disk (CD) drive or digital versatile disk ( Optical disk drives such as DVD drives, solid-state drives (SSDs), Exemplary computer storage media include computer readable instructions Any device for storing information, such as data structures, program modules, or other data. Volatile and non-volatile, removable and non-removable, implemented in any method or technology This includes media.

[0122] System memory 1606, removable storage 1636 and non-removable storage The device 1638 is an example of a computer storage medium. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage device, magnetic cassette A magnetic tape, magnetic disk storage device or other magnetic storage device or device that stores the desired information and can be used to store and access the computing device 1600. Such computer storage media may be used in conjunction with a computing device or other device. It may be part of the processor 1600.

[0123] The computing device 1600 includes a bus / interface controller 163. 0 to various interface devices (e.g., output device 1642, peripheral 1644 and communication device 1646) to the basic configuration 1602. An interface bus 1640 may also be included. Exemplary output devices 1642 include one or more A / V port 1652 allows you to communicate with various external devices such as displays or speakers. A graphics processing unit 1648 and an audio processing unit 1648 may be configured to receive Exemplary peripheral devices 1642 may include one or more I / O ports 1650. 658, an input device (e.g., a keyboard, a mouse, a pen, a voice input device, a touch External devices such as input devices (e.g., input devices, etc.) or other peripheral devices (e.g., printers, scanners, etc.) A serial interface controller 1654 or A parallel interface controller 1656 may be included. 46 communicates with the network communication link via one or more communication ports 1664. configured to facilitate communication with one or more other computing devices 1662 The network controller 1660 may be implemented as a

[0124] A network communication link is one example of a communication medium. Data-readable instructions, data structures, program modules, or carrier waves or other transport mechanisms. and may be embodied by other data in a modulated data signal, which may include any information delivery medium. A "modulated data signal" is a signal that has been set or changed in such a manner as to encode information in the signal. By way of example and not limitation, communication media may include a signal having one or more of the following characteristics: , wired media such as wired networks or direct wired connections, as well as acoustic, radio frequency (RF) ), wireless media such as microwave, infrared (IR) and other wireless media As used herein, the term computer-readable medium includes storage media and communication media. The medium may include both.

[0125] The computing device 1600 may be a mobile phone, a personal digital assistant, (PDAs), personal media player devices, wireless web watch devices personal headset device, application specific device or the functions of the above Small-format portable (or mobile) electronic devices, such as hybrid devices that include either The computing device 1600 may also be implemented as a laptop computer. Personal computers, including both laptop and non-laptop computer configurations. The computing device 1600 may be implemented in any type of network. The computing device 1600 may be a , may be an automated system as described herein.

[0126] The embodiments described herein may be implemented using various computer hardware or software modules. This may involve the use of a special purpose computer including a microprocessor module.

[0127] Embodiments within the scope of the present disclosure include computer executable instructions or The present invention also includes computer-readable media for carrying or storing data structures that include the above-mentioned components. Computer-readable media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer readable media may include RAM, ROM, ELF, PROM, CD-ROM or other optical disk storage device, magnetic disk storage device or or other magnetic storage device, or any other desired program code means, or can be stored or carried in the form of a data structure and accessed by a computer. It may also include other media. Information may be transmitted over a network or other communications connection (hardwired, wired, to a computer via a USB cable (wireless or a combination of hardwired and wireless) If transferred or provided, the computer properly treats the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.

[0128] Computer-executable instructions may, for example, cause a computer or special-purpose processing device to perform a particular function. or instructions and data for performing a group of functions. The subject matter includes structural and / or methodological features. Although described in act-specific language, the subject matter described herein may be modified in any manner to suit the particular features or characteristics set forth above. It should be understood that this is not limited to acts.

[0129] With respect to the use of virtually any plural and / or singular terminology herein, from the plural to the singular and / or from the singular to the plural as appropriate to the context and / or usage. The various singular / plural permutations are expressly set forth herein for the sake of clarity. It may be listed.

[0130] In general, the terms used herein are generally intended as "open" terms. As one of ordinary skill in the art would understand (for example, the term "including" means "including, but not limited to" " and the term "having" should be interpreted as "having at least and the term "including" should be interpreted as "including but not limited to" Moreover, if a specific number is intended, such intent must be expressly recited. In the absence of such a reference, it will be understood by those skilled in the art that no such intention exists. For example, as an aid to understanding, the descriptions provided herein may be used in conjunction with the The introductory phrases "at least one" and "one or more" to introduce method and structure elements However, the use of such phrases may be subject to the indefinite article "a" or "an " is meant to limit the description to embodiments containing only one such introduction. The same is true of the use of definite articles to introduce elements.

[0131] Additionally, when features or aspects of the present disclosure are described with reference to Markush groups, The present disclosure does not in any way constitute a representation to any individual member or entity included in the Markush Group. It will be appreciated that subgroups of configurations may also be discussed.

[0132] As will be appreciated by those skilled in the art, for any purpose, including providing a written description, All ranges disclosed herein are expressly intended to encompass any and all subranges and combinations thereof. Any ranges stated herein are intended to be at least equal halves, thirds, or combinations of the same range. , quarters, fifths, tenths, etc. As a non-limiting example, each range described herein can be expressed in terms of the lower third. It is easily divided into three parts, such as the first third, the middle third and the top third, etc. Thus, all language such as "up to," "at least," etc., includes the enumerated numbers and is consistent with the above. Finally, as will be appreciated by those of skill in the art, Thus, for example, a cell having 1 to 3 cells is in the range. A group having 1, 2 or 3 cells refers to a group having 1 to 5 cells. Group refers to groups having 1, 2, 3, 4 or 5 cells.

[0133] Thus, various embodiments of the present disclosure have been described herein for purposes of illustration, and the present disclosure It will be understood that various modifications can be made without departing from the scope and spirit of the present invention. Accordingly, the various embodiments disclosed herein are not intended to be limiting. do not have.

[0134] This disclosure is accompanied by claims, which are illustrative of the embodiments described herein. The claims of this patent application are presented solely to provide a illustrative example. The present disclosure is not intended to limit the embodiments described herein, but instead to further illustrate selected embodiments. The scope of such embodiments described herein is instead set forth in the 2018 January 1 This patent application claims priority to U.S. Provisional Application No. 62 / 754,481, filed January 1, 2014. Any other claims that may be presented in a subsequently filed nonprovisional application claiming benefit of the application. No. 60 / 639,933, filed on Oct. 23, 2006, which provisional application is specifically incorporated herein by reference in its entirety.

[0135] All references cited herein are hereby incorporated by specific reference in their entirety. It is used.

[0136] This patent application was filed on October 11, 2016 under PCT application number PCT / US16 / 5 6476, U.S. National Phase Application No. 15 / 766,805, October 9, 2015 U.S. Provisional Application No. 62 / 239,604 filed on March 13, 2018 No. 15 / 919,666, and U.S. Application No. 15 / 714,98, filed September 25, 2017. 4 and U.S. application Ser. No. 13 / 842,004, filed March 15, 2013, which are incorporated herein by reference in their entireties.

Claims

1. A computer-implemented method for identifying genetic biomarkers for a condition. , a method including: For a subject condition, a quantitative analysis of the phenotypic images is performed for a plurality of subjects having the condition. performing image analysis to obtain a first accurate quantitative phenotype; performing a quantitative genomic analysis for each subject of said plurality of subjects, or a plurality of different subjects. To do so; The condition in the plurality of subjects is identified from the quantitative image analysis and the quantitative genomic analysis. obtaining at least one candidate gene biomarker for To validate the at least one candidate gene biomarker, predicting clinical response to candidate gene biomarkers; determining at least one candidate gene biomarker for said condition; and identifying at least one therapeutic target for said condition. the therapeutic target is biologically associated with said at least one candidate gene biomarker; and Beauty, generating a report including at least one therapeutic target identified for said condition; 。

2. 10. The computer-implemented method of claim 1, Performing quantitative genomic analysis is a genome-wide association study of quantitative trait loci (QT LGWAS).

3. 10. The computer-implemented method of claim 1, To validate the at least one candidate gene biomarker, By predicting clinical response to the candidate gene biomarkers, the validation metric (a The method further comprising determining a validation metric.

4. 4. The computer-implemented method of claim 3, The prediction of clinical response is at least one genetic biomarker associated with The method is carried out to regulate another biological pathway.

5. 5. The computer-implemented method of claim 4, further comprising: The predicted clinical response is determined based on a biological sample associated with the at least one genetic biomarker. The method is carried out to inhibit at least one biologically active protein in a metabolic pathway. Law.

6. 10. The computer-implemented method of claim 1, further comprising at least one of the following: The method further comprises: obtaining said image of said phenotype of said subject, said image being of a state at a given time point; Precisely indicate the state; Obtaining phenotyping accuracy of about 85% or higher for quantitative phenotyping; detecting a disease as the condition from the image of the phenotype of the subject; Defining disease activity across the disease spectrum; or performing a standardized uptake value ratio (SUVR) analysis using said image of said phenotype of said subject thing.

7. 10. The computer-implemented method of claim 1, Performing the quantitative image analysis of the images of the phenotype comprises at least one of: Also includes one of the following methods: Calculating a voxel-based amyloid standardized uptake ratio (SUVR) from said images. ； The images were used to generate voxel-based dopamine transporter single-photon emission computed tomography (DPE) images. Calculating AT-SPECT quantification; calculating alpha-synuclein ligand binding on a voxel basis from said images; or Calculating local serotonin receptor ligand binding from said images.

8. 10. The computer-implemented method of claim 1, The imaging may be performed using techniques such as positron emission tomography (PET), magnetic resonance imaging (MRI), single photon Electron-optical emission tomography (SPECT), and combinations thereof.

9. 4. The computer-implemented method of claim 3, The predicting of clinical response comprises at least one of the following methods: Genotype quantification was performed using hippocampus-masked ) performing voxel-based tau SUVR; Voxel-based tau SUVR memory tracking using genotype quantification in the hippocampus Implementing decisions; To perform voxel-based tau quantification within the hippocampus; performing tau imaging to track clinical response; or Conducting clinical response follow-up measurements.

10. 4. The computer-implemented method of claim 3, The predicting of clinical response comprises at least one of the following methods: performing a quantification of tremor; or performing automated tremor quantification; Here, quantification of tremor is optionally by genotype.

11. 4. The computer-implemented method of claim 3, The predicting of the clinical response may include determining whether the serotonin receptor ligand is a voxel-based predictor of the clinical response from the images. The method includes calculating a bond.

12. 10. The computer-implemented method of claim 1, A genotype based on the at least one candidate gene biomarker for the condition. The method further comprises performing a phenotype prediction from the

13. 13. The computer-implemented method of claim 12, The method, wherein the prediction of the phenotype comprises at least one of the following: Deep learning phenotype prediction from single nucleotide polymorphism (SNP) models; Quantitative phenotyping by positron emission tomography (PET) imaging; Predicting voxel-based amyloid SUVR with deep learning SNP models; Predicting voxel-based DAT-SPECT quantification with deep learning SNP models ； Predicting α-synuclein ligand binding on a voxel basis using deep learning SNP models or Predicting regional serotonin receptor ligand binding with deep learning SNP models.

14. 10. The computer-implemented method of claim 1, A method comprising at least one of the following: placing said at least one candidate gene biomarker in said report; analyzing a biological pathway having said at least one candidate gene biomarker; Or, Associating the at least one candidate gene biomarker with a biological pathway associated with the condition. To compare.

15. 10. The computer-implemented method of claim 1, The method, wherein the condition is: a syndrome having a set of interrelated medical signs and symptoms in said subject; A disease that exhibits a pathophysiological response to factors external or internal to the subject; or A disorder that disrupts the normal bodily structure and / or function of said subject.

16. 16. The computer-implemented method of claim 15, The condition is selected from Alzheimer's disease, Parkinson's disease, and major depressive disorder (MDD). The method to be selected.

17. 1. A system for identifying genetic biomarkers for a condition, comprising: The system includes: Deep Phenotyping Unit; A QTL GWAS analysis unit downstream of the deep phenotyping unit; and A clinical response validation unit downstream of said QTL GWAS analysis unit.

18. 20. The system of claim 17, The components of the system are comprised of one or more of the following: Stem: The deep phenotyping unit is configured to perform at least one of the following: It is used: To perform accurate quantitative phenotypic analysis; Calculating voxel-based amyloid SUVR from PET and T1 MRI images and; Voxel-based DAT-SPECT quantification was performed from SPECT and T1 MRI images. To calculate; Voxel-based α-synuclein ligand binding from PET and T1 MRI images calculating; or Calculating local serotonin receptor ligand binding from PET and T1 MRI images and; The QTL GWAS analysis unit performs genome-wide association analysis of quantitative trait loci (QTL GWAS). TL GWAS); and The clinical response verification unit is configured to perform at least one of the following: Has: To validate the at least one candidate gene biomarker, To predict clinical response to three candidate gene biomarkers; To compare hippocampal masked voxel-based tau SUVR by genotype; Comparing tremor quantification by genotype; or Voxel-based serotonin receptor ligand binding was analyzed using PET and T1 MRI images. Use this to make calculations.

19. 20. The system of claim 18, The system further includes at least one of the following: To perform gene expression profiling, either automated or non-automated A gene expression analysis unit was constructed; One or more biological pathways of the genes identified by the gene expression analysis unit. a path mapping unit configured to map; Using a deep learning model to predict the phenotype identified by the deep phenotyping unit. or to predict new phenotypes for new investigations of new conditions. phenotype prediction units; Optionally, in conjunction with the deep phenotyping unit, a phenotypic pattern or trend may be determined. a pattern and / or trend analysis unit configured to determine Statistical analysis configured to perform statistical analysis of any data or metrics of the method unit; To determine the relevance of therapeutic targets by text mining and / or to The method comprises the steps of: determining chemical modulators of biological entities of biological pathways associated with a target; The text mining validity analysis unit was created; configured to design or generate new chemical entities (NCEs) capable of modulating said therapeutic target. or a new chemical entity (NCE) prediction unit; or The reporting unit is configured to generate a report.

20. Artificial Neural Networks for Detecting Amyloid Positive or Early Alzheimer's Disease 1. A computer-implemented method for training a neural network for predicting a neural network, the method comprising: From the database, corresponding positron emission tomography (PET) images, magnetic resonance imaging (MRI) images, collecting a set of RI) images and corresponding genotype data; Calculate voxel-based amyloid SUVR from the PET and T1 MRI images to obtain accurate quantitative phenotypes of early Alzheimer's disease; generating a training set comprising said quantitative phenotype and corresponding genotype data; and Beauty, The training set is used to predict the quantitative phenotype from genotype data. Performing training of the neural network.

21. 21. The computer-implemented method of claim 20, The artificial neural network is configured to select at least a subset of SNP ids 1-32. and optionally a SNP id 33 for predicting said quantitative phenotype. Trained using offspring data,method: ID Chr SNP A1 A2 1 1 rs4912453 T C 2 1 rs12120406 G A 3 1 rs11161719 C T 4 1 rs6576798 C T 5 3 rs2030515 G A 6 4 rs4689137 G C 7 4 rs10007765 C G 8 4 rs10029820 G A 9 6 rs9458512 A G 10 7 rs1001029 A G 11 7 rs1001026 A G 12 7 rs13222318 C T 13 7 rs62444137 A G 14 7 rs17680408 A G 15 7 rs10234008 T C 16 7 rs917321 T C 17 7 rs993900 A G 18 14 rs8009420 A C 19 14 rs213563 A G 20 15 rs7164265 C T 21 15 rs1551466 C G 22 15 rs12916234 A C 23 15 rs9920618 C T 24 16 rs8056050 C T 25 18 rs4891826 G T 26 19 rs71352238 C T 27 19 rs2075650 G A 28 19 rs157582 A G 29 19 rs769449 A G 30 19 rs56131196 A G 31 19 rs4420638 G A 32 19 rs17815373 A G 33 16 rs4402561 C T.

22. A computer-implemented method, comprising: Calculating voxel-based amyloid SUVR from PET and T1 MRI images ； Conducting quantitative trait locus genome-wide association studies (QTL GWAS); Quantitative imaging and quantitative genomic analysis provide at least some insight into the condition in multiple subjects. To obtain a candidate gene biomarker; To compare hippocampal masked voxel-based tau SUVR by genotype; Predicting voxel-based amyloid SUVR using deep learning SNP models; and Beauty, Generating a report including the predicted voxel-based amyloid SUVR.

Images