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Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources

  • Ping Zhang
  • Pankaj Agarwal
  • Zoran Obradovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8190)

Abstract

Drug repositioning helps identify new indications for marketed drugs and clinical candidates. In this study, we proposed an integrative computational framework to predict novel drug indications for both approved drugs and clinical molecules by integrating chemical, biological and phenotypic data sources. We defined different similarity measures for each of these data sources and utilized a weighted k-nearest neighbor algorithm to transfer similarities of nearest neighbors to prediction scores for a given compound. A large margin method was used to combine individual metrics from multiple sources into a global metric. A large-scale study was conducted to repurpose 1007 drugs against 719 diseases. Experimental results showed that the proposed algorithm outperformed similar previously developed computational drug repositioning approaches. Moreover, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models.

Keywords

Drug Repositioning Drug Indication Prediction Multiple Data Sources Metric Integration Large Margin Method 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ping Zhang
    • 1
  • Pankaj Agarwal
    • 2
  • Zoran Obradovic
    • 3
  1. 1.Healthcare Analytics ResearchIBM T.J. Watson Research CenterUSA
  2. 2.Computational BiologyGlaxoSmithKline R&DUSA
  3. 3.Center for Data Analytics and Biomedical InformaticsTemple UniversityUSA

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