Prioritizing disease genes by bi-random walk

Maoqiang Xie, Taehyun Hwang, Rui Kuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Number of pages12
EditionPART 2
StatePublished - May 29 2012
Event16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
Duration: May 29 2012Jun 1 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
CityKuala Lumpur


  • Bi-Random Walk
  • Disease Gene Prioritization
  • Graph-based Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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