Using random walks to identify cancer-associated modules in expression data.

ABSTRACT:

Background

The etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.

Results

We implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular ca

SUBMITTER: Petrochilos D 

PROVIDER: S-EPMC4015830 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature