Siqi Liang1,2, Haiyuan Yu1,2. 1. Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA. 2. Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.
Abstract
MOTIVATION: 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. RESULTS: 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 potentially 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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 potentially 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Seth D Axen; Xi-Ping Huang; Elena L Cáceres; Leo Gendelev; Bryan L Roth; Michael J Keiser Journal: J Med Chem Date: 2017-08-08 Impact factor: 7.446
Authors: Michael J Keiser; Vincent Setola; John J Irwin; Christian Laggner; Atheir I Abbas; Sandra J Hufeisen; Niels H Jensen; Michael B Kuijer; Roberto C Matos; Thuy B Tran; Ryan Whaley; Richard A Glennon; Jérôme Hert; Kelan L H Thomas; Douglas D Edwards; Brian K Shoichet; Bryan L Roth Journal: Nature Date: 2009-11-01 Impact factor: 49.962
Authors: W H Bisson; A V Cheltsov; N Bruey-Sedano; B Lin; J Chen; N Goldberger; L T May; A Christopoulos; J T Dalton; P M Sexton; X-K Zhang; R Abagyan Journal: Proc Natl Acad Sci U S A Date: 2007-07-02 Impact factor: 11.205