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Sci Rep. 2015 Jul 21;5:12339. doi: 10.1038/srep12339.

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

Author information

1
Healthcare Analytics Research, IBM T. J. Watson Research Center, Yorktown Heights, USA.
2
Department of Computer Science and Engineering, University of Connecticut, Storrs, USA.

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 that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html.

PMID:
26196247
PMCID:
PMC5387872
DOI:
10.1038/srep12339
[Indexed for MEDLINE]
Free PMC Article

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