RT Journal Article SR Electronic T1 Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning JF bioRxiv FD Cold Spring Harbor Laboratory SP 572420 DO 10.1101/572420 A1 Siqi Liang A1 Haiyuan Yu YR 2019 UL http://biorxiv.org/content/early/2019/03/09/572420.abstract AB In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity, that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.