Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.

ABSTRACT: BACKGROUND:Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS:We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate th

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC6933655 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature