SYSTEM TO PREDICT DRUG INDICATION AND RESPONSE USING ARTIFICIAL INTELLIGENCE DEEP LEARNING MODEL BASED ON CONVERGENCE OF DIFFERENT CATEGORY DATA AND METHOD THEREOF

摘要

The present invention relates to cancer-drug response scan (CDRscan), that is a system and method to predict drug indication and response, which is a new learning model capable of reliably predicting drug response by genetic variation fingerprints related to diseases including cancer and combination analysis of a molecular profile of a drug. According to the present invention, the system comprises: a learning module performing, by deep learning machine learning, learning response correlation of configuration information forming a drug with respect to genetic information included in a genome from collected learning information; a prediction module receiving analysis information to calculate a response prediction result of the drug with respect to the genome included in the analysis information; and a storage module storing a response prediction algorithm learned by the learning module. The learning information is response information of the drug with respect to target protein, in vitro cell lines, and in vivo clinical researches. Accordingly, provided is an effect of predicting the degree of genome-drug responses with an unidentified medicinal effect from response results of the drug with respect to the genome, which are collected from a clinical experiment.;COPYRIGHT KIPO 2019