Prioritizing Disease Genes by Bi-Random Walk

  • Maoqiang Xie
  • Taehyun Hwang
  • Rui Kuang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

Random walk methods have been successfully applied to prioritizing disease causal genes. In this paper, we propose a bi-random walk algorithm (BiRW) based on a regularization framework for graph matching to globally prioritize disease genes for all phenotypes simultaneously. While previous methods perform random walk either on the protein-protein interaction network or the complete phenome-genome heterogenous network, BiRW performs random walk on the Kronecker product graph between the protein-protein interaction network and the phenotype similarity network. Three variations of BiRW that perform balanced or unbalanced bi-directional random walks are analyzed and compared with other random walk methods. Experiments on analyzing the disease phenotype-gene associations in Online Mendelian Inheritance in Man (OMIM) demonstrate that BiRW effectively improved disease gene prioritization over existing methods by ranking more known associations in the top 100 out of nearly 10,000 candidate genes.

Keywords

Disease Gene Prioritization Bi-Random Walk Graph-based Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maoqiang Xie
    • 1
  • Taehyun Hwang
    • 2
  • Rui Kuang
    • 3
  1. 1.College of SoftwareNankai UniversityTianjinChina
  2. 2.Masonic Cancer CenterUniversity of MinnesotaTwin CitiesUSA
  3. 3.Department of Computer Science and EngineeringUniversity of MinnesotaTwin CitiesUSA

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