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

Zhang H, Song X, Wang H, Zhang X - J. Biomed. Biotechnol. (2010)

Bottom Line: Computational analysis of microarray data has provided an effective way to identify disease-related genes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, China.

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.

Show MeSH

Related in: MedlinePlus

Images of the Pearson correlation coefficient matrices for the eight genes from colon dataset. (a) Normal samples. (b) Disease samples.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2822236&req=5

fig8: Images of the Pearson correlation coefficient matrices for the eight genes from colon dataset. (a) Normal samples. (b) Disease samples.

Mentions: Figures 7 and 8 show Euclidean distance values matrices and Pearson correlation coefficient values matrices of the eight genes identified by MIClique from colon dataset respectively. The Euclidean distance values range from 2.025 to 7.073 in normal samples and range from 1.676 to 5.497 in disease samples. The Pearson correlation coefficient values range from 0.151 to 0.946 in normal samples and range from 0.242 to 0.891 in disease samples. Both of the figures display no indication of differentially coexpression patterns among the eight genes.


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

Zhang H, Song X, Wang H, Zhang X - J. Biomed. Biotechnol. (2010)

Images of the Pearson correlation coefficient matrices for the eight genes from colon dataset. (a) Normal samples. (b) Disease samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2822236&req=5

fig8: Images of the Pearson correlation coefficient matrices for the eight genes from colon dataset. (a) Normal samples. (b) Disease samples.
Mentions: Figures 7 and 8 show Euclidean distance values matrices and Pearson correlation coefficient values matrices of the eight genes identified by MIClique from colon dataset respectively. The Euclidean distance values range from 2.025 to 7.073 in normal samples and range from 1.676 to 5.497 in disease samples. The Pearson correlation coefficient values range from 0.151 to 0.946 in normal samples and range from 0.242 to 0.891 in disease samples. Both of the figures display no indication of differentially coexpression patterns among the eight genes.

Bottom Line: Computational analysis of microarray data has provided an effective way to identify disease-related genes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, China.

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.

Show MeSH
Related in: MedlinePlus