title: 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 similar function. By applying the MIClique algorithm to real gene expression data, some DEC gene subsets which correlated under one experimental condition but uncorrelated under another condition are detected from the graph of colon dataset and leukemia dataset.
authors.affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, China.
authors.forename: Huanping
authors.initials: H
authors.lastname: Zhang
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Song
authors.forename: Huinan
authors.initials: H
authors.lastname: Wang
authors.forename: Xiaobai
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003110:Colonic Neoplasms / Q000235:genetics; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D019277:Entropy; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007938:Leukemia / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*