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BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

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  • Xiujuan Lei
  • Wenxiang Zhang

Abstract

The circular RNAs (circRNAs) have significant effects on a variety of biological processes, the dysfunction of which is closely related to the emergence and development of diseases. Therefore, identification of circRNA-disease associations will contribute to analysing the pathogenesis of diseases. Here, we present a computational model called BRWSP to predict circRNA-disease associations, which searches paths on a multiple heterogeneous network based on biased random walk. Firstly, BRWSP constructs a multiple heterogeneous network by using circRNAs, diseases, and genes. Then, the biased random walk algorithm runs on the multiple heterogeneous network to search paths between circRNAs and diseases. Finally, the performance of BRWSP is significantly better than the state-of-the-art algorithms. Furthermore, BRWSP further contributes to the discovery of novel circRNA-disease associations.

Suggested Citation

  • Xiujuan Lei & Wenxiang Zhang, 2019. "BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network," Complexity, Hindawi, vol. 2019, pages 1-12, November.
    • Handle: RePEc:hin:complx:5938035
    DOI: 10.1155/2019/5938035
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