A Multi-Label Learning Framework for Drug Repurposing.

ABSTRACT: Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l₂

SUBMITTER: Mei S 

PROVIDER: S-EPMC6781509 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature