Abstract: The present disclosure provides a system configured to identify a genetic disease and discover a disease-associated genetic variant, the system including a multiple instance learning model unit configured to derive identification of a genetic disease of a patient and discovery of a disease-associated genetic variant together using a multiple instance learning model configured to learn instances which are genetic variant information of the patient and a bag of the instances as input data and process, as a bag label, whether a disease of the patient is a genetic disease caused by a genetic variant.

Abstract: Disclosed herein is a system for predicting a loss of the function of genetic variants. The system includes a loss of function (LoF) prediction unit for calculating a probability that a target genetic variant will cause a loss of function (LoF) in a target gene through logistic regression with respect to a first probability that the target gene will be intolerant of the loss of function and a second probability that the target genetic variant contained in the target gene will be intolerant.

Abstract: The present disclosure provides a system for Pathogenicity prediction of a genomic variant using knowledge transfer, wherein the system is configured to learn an artificial neural network model using virtual genomic variant data generated from evolutionary conservation data, and learn actual genomic variant data by transferring knowledge by sharing weight values of a hidden layer extracted from the artificial neural network model to the artificial neural network model.

Abstract: The present disclosure provides a system for discovering a novel target protein and a companion diagnostic biomarker therefor, the system comprising: a patient group classification unit specifying a single gene and dividing a high-expression patient group and a low-expression patient group according to an expression level of the single gene; a prognostic association calculation unit calculating prognostic association values of all genes in the high-expression patient group and the low-expression patient group; a prognostic association comparison unit comparing prognostic association values of the high-expression patient group and prognostic association values of the low-expression patient group for all genes; and a biomarker selection unit selecting a biomarker to divide a patient group from the comparison value.