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• W2022165721 startingPage "1023" @default.
• W2022165721 abstract "This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification." @default.
• W2022165721 created "2016-06-24" @default.
• W2022165721 creator A5023487576 @default.
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• W2022165721 date "2008-02-19" @default.
• W2022165721 modified "2023-10-06" @default.
• W2022165721 title "Approaches to working in high-dimensional data spaces: gene expression microarrays" @default.
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• W2022165721 doi "https://doi.org/10.1038/sj.bjc.6604207" @default.
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