Network-based phenome-genome association prediction by bi-random walk

Mao Qiang Xie, Ying Jie Xu, Yao Gong Zhang, Tae Hyun Hwang, Rui Kuang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Motivation: The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions. Results: The analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRWeffectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.

Original languageEnglish (US)
Article numbere0125138
JournalPloS one
Issue number5
StatePublished - May 1 2015

Bibliographical note

Publisher Copyright:
© 2015 Xie et al.


Dive into the research topics of 'Network-based phenome-genome association prediction by bi-random walk'. Together they form a unique fingerprint.

Cite this