Extracting gene networks for low-dose radiation using graph theoretical algorithms.

ABSTRACT: Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from

SUBMITTER: Voy BH 

PROVIDER: S-EPMC1513268 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature