ArticleJournal of biomedicine & biotechnology2009
MIClique: An algorithm to identify differentially coexpressed disease gene subset from microarray data.
Article in Journal of biomedicine & biotechnology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.BioMed research international · 2016Pooled it
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- MIRA: mutual information-based reporter algorithm for metabolic networks.Bioinformatics (Oxford, England) · 2014Article
- Navigating traditional chinese medicine network pharmacology and computational tools.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- ADEMA: an algorithm to determine expected metabolite level alterations using mutual information.PLoS computational biology · 2013Article
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- Using graph theory to analyze biological networks.BioData mining · 2011Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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