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Disease gene identification by using graph kernels and Markov random fields

Science China Life Sciences volume 57, pages 1054–1063 (2014)Cite this article

Abstract

Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.

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Authors and Affiliations

  1. Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, Canada

    BoLin Chen & Fang-Xiang Wu

  2. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Min Li & JianXin Wang

  3. Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, Canada

    Fang-Xiang Wu

Authors
  1. BoLin Chen
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  2. Min Li
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  3. JianXin Wang
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  4. Fang-Xiang Wu
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Correspondence to Fang-Xiang Wu.

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Chen, B., Li, M., Wang, J. et al. Disease gene identification by using graph kernels and Markov random fields. Sci. China Life Sci. 57, 1054–1063 (2014). https://doi.org/10.1007/s11427-014-4745-8

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  • DOI: https://doi.org/10.1007/s11427-014-4745-8

Keywords

  • disease gene identification
  • data integration
  • Markov random field
  • graph kernel
  • Bayesian analysis