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Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering, classification, and identification. The theoretical frameworks of ICA have been described to furtherdoi:10.2144/000112950 pmid:19007336 pmcid:PMC3005719 fatcat:vcdoxu4ecjginfjj7pkrqvc7v4