title: A review of independent component analysis application to microarray gene expression data.
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.
authors.affiliation: Information Engineering College, Shanghai Maritime University, Shanghai, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Kong
authors.forename: Charles R
authors.initials: CR
authors.lastname: Vanderburg
authors.forename: Hiromi
authors.initials: H
authors.lastname: Gunshin
authors.forename: Jack T
authors.initials: JT
authors.lastname: Rogers
authors.forename: Xudong
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D006801:Humans; D016018:Least-Squares Analysis; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015398:Signal Transduction