Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data.

ABSTRACT:

Background

High throughput experiments resulted in many genomic datasets and hundreds of candidate disease genes. To discover the real disease genes from a set of candidate genes, computational methods have been proposed and worked on various types of genomic data sources. As a single source of genomic data is prone of bias, incompleteness and noise, integration of different genomic data sources is highly demanded to accomplish reliable disease gene identification.

Results

In contrast to the commonly adapted data integration approach which integrates separate lists of candidate genes derived from the each single data sources, we merge various genomic networks into a multigraph which is capable of connecting multiple edges between a pair of nodes. This novel approach provides

SUBMITTER: Li Y 

PROVIDER: S-EPMC3521411 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature