Predicting drug target interactions using meta-path-based semantic network analysis.

Journal: BMC bioinformatics
Published Date: Apr 12, 2016

Abstract

BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.

Authors

  • Gang Fu
    PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA.
  • Ying Ding
    Cockrell School of Engineering, The University of Texas at Austin, Austin, USA.
  • Abhik Seal
    School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA.
  • Bin Chen
    Department of Otorhinolaryngology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, China.
  • Yizhou Sun
    College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, USA.
  • Evan Bolton
    PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA.

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