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)


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.


Disease Gene Prioritization Bi-Random Walk Graph-based Learning 


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  1. 1.
    Consortium The Wellcome Trust Case Control. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007)Google Scholar
  2. 2.
    Johnson, A., O’Donnell, C.: An open access database of genome-wide association resutls. BMC Med. Gent. 10, 6 (2009)CrossRefGoogle Scholar
  3. 3.
    Franke, L., Bakel, H., Fokkens, L., et al.: Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 1011–1025 (2006)CrossRefGoogle Scholar
  4. 4.
    Köhler, S., Bauer, S., Horn, D., et al.: Walking the Interactome for Prioritization of Candidate Disease Genes. Am. J. Hum. Genet. 82, 949–958 (2008)CrossRefGoogle Scholar
  5. 5.
    Wu, X.B., Jiang, R., Zhang, M.Q., et al.: Network-based global inference of human disease genes. Mol. Syst. Biol. 4 (2008)Google Scholar
  6. 6.
    Linghu, B., Snitkin, E.S., Hu, Z., et al.: Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol. 10, R91 (2009)Google Scholar
  7. 7.
    Hwang, T.H., Kuang, R.: A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery. In: Proc. of SIAM Intl. Conf. on Data Mining, pp. 583–594 (2010)Google Scholar
  8. 8.
    Vanunu, O., Magger, O., Ruppin, E., et al.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6, e1000641 (2010)Google Scholar
  9. 9.
    Li, Y., Patra, J.C.: Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network. Bioinformatics 26, 1219–1224 (2010)CrossRefGoogle Scholar
  10. 10.
    van Driel, M.A., Bruggeman, J., Vriend, G., et al.: A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 14, 535–542 (2006)CrossRefGoogle Scholar
  11. 11.
    McKusick, V.A.: Mendelian inheritance in man and its online version, OMIM. Am. J. Hum. Genet. 80, 588–604 (2007)CrossRefGoogle Scholar
  12. 12.
    Peri, S., Navarro, J.D., Amanchy, R., et al.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13, 2363–2371 (2003)CrossRefGoogle Scholar
  13. 13.
    Chuang, H., Lee, E., Liu, Y., et al.: Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007)CrossRefGoogle Scholar
  14. 14.
    Singh, R., Xu, J., Berger, B.: Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology. Res. in Comp. Mol. Biol. 4453, 16–31 (2007)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Zhang, S., Wang, Y., et al.: Alignment of molecular networks by integer quadratic programming. Bioinformatics 23, 1631–1639 (2007)CrossRefGoogle Scholar
  16. 16.
    Guo, X., Hartemink, A.J.: Domain-oriented edge-based alignment of protein interaction networks. Bioinformatics 25, i240–i246 (2009)Google Scholar
  17. 17.
    Zaslavskiy, M., Bach, F., Vert, J.P.: Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics 25, i259–i267 (2009)Google Scholar
  18. 18.
    Singh, R., Xu, J., Berger, B.: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc. Natl. Acad. Sci. U.S.A. 105, 12763–12768 (2008)CrossRefGoogle Scholar
  19. 19.
    Zhou, D., et al.: Learning with Local and Global Consistency. Advanced Neural Information Processing Systems 16, 321–328 (2004)Google Scholar
  20. 20.
    Chua, H., Sung, W., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22, 1623–1630 (2006)CrossRefGoogle Scholar
  21. 21.
    Xu, J., Li, Y.: Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22, 2800–2805 (2006)CrossRefGoogle Scholar

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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