MIClique: An algorithm to identify differentially coexpressed disease gene subset from microarray data.

ABSTRACT: Computational analysis of microarray data has provided an effective way to identify disease-related genes. Traditional disease gene selection methods from microarray data such as statistical test always focus on differentially expressed genes in different samples by individual gene prioritization. These traditional methods might miss differentially coexpressed (DCE) gene subsets because they ignore the interaction between genes. In this paper, MIClique algorithm is proposed to identify DEC gene subsets based on mutual information and clique analysis. Mutual information is used to measure the coexpression relationship between each pair of genes in two different kinds of samples. Clique analysis is a commonly used method in biological network, which generally represents biological module of

SUBMITTER: Zhang H 

PROVIDER: S-EPMC2822236 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature