Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

ABSTRACT: In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where

SUBMITTER: Liu Y 

PROVIDER: S-EPMC4752318 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature