A method (100) for characterizing a functional impact of a plurality of variants, comprising: obtaining (110) information comprising at least a plurality of variants, gene expression information, copy number variation, and epigenetic effects; determining (120) a splice status for the variant; determining (130) a variant-based expression regulation status, comprising whether the variant has an effect on gene expression; determining (140) a gene-based expression regulation status, comprising an indication of whether the variant has a functional impact on a target gene; determining (150) a gene-based copy number variant (CNV) and epigenetic impact status, comprising whether one or both has an impact on expression of a gene; adjusting (160), based on the CNV and epigenetic impact status, the variant-based and/or the gene-based expression regulation status; and reporting (170) at least the adjusted variant-based and/or the adjusted gene-based expression regulation status for each of a plurality of variants and/or genes from the genomic sample.

The present invention relates to: a method for providing information for choosing a breast cancer treatment method by using a breast cancer ultrasonic image and gene information; a system for choosing a breast cancer treatment method by using a breast cancer ultrasonic image; and a method for providing information required for predicting the prognosis of a breast cancer patient.

Systems and methods for associating a compound with physiological conditions are provided. A fingerprint of a compound chemical structure is obtained and inputted to a model that outputs one or more calculated activation scores. Each activation score represents a cellular constituent module in a set of modules, where each module includes a subset of cellular constituents and a first module in the set of modules is associated with the physiological condition. When the activation score for the first module satisfies a threshold criterion, the compound is identified as associated with the physiological condition. In some aspects, each activation score represents a perturbation signature associated with the physiological condition and the compound is identified when the activation score for a first perturbation signature satisfies a threshold criterion. Systems and methods for training a model that associates compounds with physiological conditions are also provided.

Disclosed are novel compounds for treating and preventing schizophrenia, and more generally psychosis, by bioinformatics drug repurposing using novel genes expression biomarkers involved in psychotic symptoms (delusions, hallucinations); methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for psychosis in a subject; and method of using repurposed drugs and natural compounds to prevent and to treat psychosis. Methods are disclosed using a universal approach, in everybody, as well as personalized approaches by gender. The discovery describes compounds for use in everybody (universal), as well as personalized by gender (males, females). Methods for identifying which subjects should be receiving which treatment, using genes expression biomarkers for patient stratification and measuring response to treatment. The disclosure also relates to algorithms. The algorithms combine biomarkers as well as clinical measures for psychosis, to identify subjects who are at risk of psychosis, and to track responses to treatments.

Disclosed are novel compounds for treating and preventing schizophrenia, and more generally psychosis, by bioinformatics drug repurposing using novel genes expression biomarkers involved in psychotic symptoms (delusions, hallucinations); methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for psychosis in a subject; and method of using repurposed drugs and natural compounds to prevent and to treat psychosis. Methods are disclosed using a universal approach, in everybody, as well as personalized approaches by gender. The discovery describes compounds for use in everybody (universal), as well as personalized by gender (males, females). Methods for identifying which subjects should be receiving which treatment, using genes expression biomarkers for patient stratification and measuring response to treatment. The disclosure also relates to algorithms. The algorithms combine biomarkers as well as clinical measures for psychosis, to identify subjects who are at risk of psychosis, and to track responses to treatments.

The present invention relates to a biomarker for diagnosis of overactive bladder (OAB) disease, and a method for screening a drug using the biomarker. The markers described in the present invention can effectively detect or diagnose the onset of OAB by distinguishing them from normal populations. In particular, OAB-specific protein markers released into urine enable simple and rapid OAB diagnosis in a non-invasive manner. In addition, by selecting an agent that changes, particularly normalizes the expression and activity of the markers selected in the present invention, more effective preventative or therapeutic agents of OAB disease can be screened.

Aspects of the disclosure relate to improved methods and systems for active surveillance of subject having non-aggressive prostate cancer.

Provided herein are methods for detecting cells in a subject that express aberrant proteins. Methods are also provided for eliminating such cells expressing aberrant proteins.

Systems and methods for associating cellular constituents with a cellular process of interest are provided. Constituent vectors comprising abundances for a first plurality of cells representing annotated cell states are formed and used to obtain a latent representation of constituent modules having subsets of constituents. A constituent count data structure comprising abundances of the constituents for a second plurality of cells representing covariates of interest is obtained. An activation data structure is formed by combining the latent representation and the constituent count data structure, using constituents as a common dimension. A model is trained using a difference between the predicted and actual absence or presence of each covariate in each cellular constituent module represented in the activation data structure, thus adjusting covariate weights indicating a correlation between covariates and constituent modules across the activation data structure. The covariate weights are used to identify constituent modules associated with covariates of interest.

Systems and methods for associating cellular constituents with a cellular process of interest are provided. Constituent vectors comprising abundances for a first plurality of cells representing annotated cell states are formed and used to obtain a latent representation of constituent modules having subsets of constituents. A constituent count data structure comprising abundances of the constituents for a second plurality of cells representing covariates of interest is obtained. An activation data structure is formed by combining the latent representation and the constituent count data structure, using constituents as a common dimension. A model is trained using a difference between the predicted and actual absence or presence of each covariate in each cellular constituent module represented in the activation data structure, thus adjusting covariate weights indicating a correlation between covariates and constituent modules across the activation data structure. The covariate weights are used to identify constituent modules associated with covariates of interest.