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A Review of Independent Component Analysis Application to Microarray Gene Expression Data

Year: 2008
Type: review
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.
DOI: 10.2144/000112950
Institutions: Brigham and Women's Hospital, Harvard University, Shanghai Maritime University, Harvard NeuroDiscovery Center, Massachusetts General Hospital, University of Massachusetts Amherst
Language: English
Open Access status: gold
Estimated APC paid: $1,415
OpenAlex ID: W2043399591
FWCI: 0.46
Cites: 74
Cited by: 111
Related to: 10