Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.

ABSTRACT: In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical mode

SUBMITTER: Yang F 

PROVIDER: S-EPMC4730876 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature