Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network

Methods. 2018 Aug 1;145:51-59. doi: 10.1016/j.ymeth.2018.06.001. Epub 2018 Jun 4.

Abstract

Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attracts wide spread interest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

Keywords: Association profile; Drug-disease association; Linear neighborhood similarity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Drug Discovery / methods*
  • Drug Repositioning / methods*
  • Humans