Network-based Phenome-Genome Association Prediction by Bi-Random Walk.

ABSTRACT:

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 ge

SUBMITTER: Xie M 

PROVIDER: S-EPMC4416812 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature