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• W2043399591 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." @default.
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• W2043399591 date "2008-11-01" @default.
• W2043399591 modified "2023-10-18" @default.
• W2043399591 title "A review of independent component analysis application to microarray gene expression data" @default.
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• W2043399591 doi "https://doi.org/10.2144/000112950" @default.
• W2043399591 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3005719" @default.
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