A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

ABSTRACT: The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interacti

SUBMITTER: Luo Y 

PROVIDER: S-EPMC5603535 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature