A review of independent component analysis application to microarray gene expression data. (November 2008)

• Record Type: Journal Article
• Authors: Kong, Wei; Vanderburg, Charles R.; Gunshin, Hiromi; Rogers, Jack T.; Huang, Xudong
• Is Part Of: Biotechniques. Volume 45:Number 5(2008)
• Page Start: 501
• Page End: 520
• Publication Date: 2008-11
• Subjects: Biology, Experimental -- Periodicals; Molecular biology -- Periodicals; Medical technology -- Periodicals; Biology, Experimental; Medical technology; Molecular biology; Clinical Laboratory Techniques -- Periodicals; Research -- Periodicals; Medical Laboratory Science -- Periodicals; Periodicals; Electronic journals; 570
• DOI: 10.2144/000112950
• Languages: English
• ISSNs: 0736-6205

Abstract

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 further illustrate its feature extraction function in microarray data analysis.