Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.

ABSTRACT: Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming t

SUBMITTER: Zhang P 

PROVIDER: S-EPMC5387872 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature