Prioritizing Disease Genes by Bi-Random Walk
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.
KeywordsDisease Gene Prioritization Bi-Random Walk Graph-based Learning
Unable to display preview. Download preview PDF.
- 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
- 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.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.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.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
- 16.Guo, X., Hartemink, A.J.: Domain-oriented edge-based alignment of protein interaction networks. Bioinformatics 25, i240–i246 (2009)Google Scholar
- 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
- 19.Zhou, D., et al.: Learning with Local and Global Consistency. Advanced Neural Information Processing Systems 16, 321–328 (2004)Google Scholar