Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity.

ABSTRACT: In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 di

SUBMITTER: Zhang P 

PROVIDER: S-EPMC4419869 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature