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Analysis of human genes with protein–protein interaction network for detecting disease genes

Author

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  • Wu, Shun-yao
  • Shao, Feng-jing
  • Sun, Ren-cheng
  • Sui, Yi
  • Wang, Ying
  • Wang, Jin-long

Abstract

The topological features of disease genes and non-disease genes were widely utilized in disease genes prediction. However, previous studies neglected to exploit essential genes to distinguish disease genes and non-disease genes. Therefore, this paper firstly takes essential genes as reference to analyze the topological properties of human genes with protein–protein interaction network. Empirical results demonstrate that nonessential disease genes are topologically more important and closer to the center of the network than other genes (unknown genes, which are deemed as non-disease genes in disease genes prediction). Although disease genes are closer to essential genes, we find that the influence of disease genes on essential genes is similar with other genes, or even weaker. Further, we generate new topological features according to our findings and validate the effectiveness of combining the additional features for detecting disease genes. In addition, we find that the k-shell index (ks) of protein–protein network follows a power law distribution, and the function of the proteins with the largest ks may deserve further research.

Suggested Citation

  • Wu, Shun-yao & Shao, Feng-jing & Sun, Ren-cheng & Sui, Yi & Wang, Ying & Wang, Jin-long, 2014. "Analysis of human genes with protein–protein interaction network for detecting disease genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 217-228.
    • Handle: RePEc:eee:phsmap:v:398:y:2014:i:c:p:217-228
    DOI: 10.1016/j.physa.2013.12.046
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    Cited by:

    1. Cui, Ying & Cai, Meng & Stanley, H. Eugene, 2018. "Discovering disease-associated genes in weighted protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 53-61.

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