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      Computational drug repositioning with random walk on a heterogeneous network

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          Abstract

          Drug repositioning is an efficient and promising strategy to identify new indications for existing drugs, which can improve the productivity of traditional drug discovery and development. Rapid advances in high-throughput technologies have generated various types of biomedical data over the past decades, which lay the foundations for furthering the development of computational drug repositioning approaches. Although many researches have tried to improve the repositioning accuracy by integrating information from multiple sources and different levels, it is still appealing to further investigate how to efficiently exploit valuable data for drug repositioning. In this study, we propose an efficient approach, Random Walk on a Heterogeneous Network for Drug Repositioning (RWHNDR), to prioritize candidate drugs for diseases. First, an integrated heterogeneous network is constructed by combining multiple sources including drugs, drug targets, diseases and disease genes data. Then, a random walk model is developed to capture the global information of the heterogeneous network. RWHNDR takes advantage of drug targets and disease genes data more comprehensively for drug repositioning. The experiment results show that our approach can achieve better performance, compared with other state-of-the-art approaches which prioritized candidate drugs based on multi-source data.

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          Author and article information

          Journal
          IEEE/ACM Transactions on Computational Biology and Bioinformatics
          IEEE/ACM Trans. Comput. Biol. and Bioinf.
          Institute of Electrical and Electronics Engineers (IEEE)
          1545-5963
          1557-9964
          2374-0043
          2018
          : 1
          Article
          10.1109/TCBB.2018.2832078
          29994051
          7614468b-2f73-4ac4-afbb-8c018b15066f
          © 2018
          History

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