Abstract
Prediction of the diagnostic category of a tissue sample from its gene-expression profile and selection of relevant genes for class prediction have important applications in cancer research. We have developed the uncorrelated shrunken centroid (USC) and error-weighted, uncorrelated shrunken centroid (EWUSC) algorithms that are applicable to microarray data with any number of classes. We show that removing highly correlated genes typically improves classification results using a small set of genes.
Publication types
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Research Support, U.S. Gov't, P.H.S.
MeSH terms
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Algorithms*
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Breast Neoplasms / diagnosis
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Breast Neoplasms / genetics
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Cell Line, Tumor
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Female
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Gene Expression Profiling / classification
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Gene Expression Profiling / statistics & numerical data*
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Gene Expression Regulation, Neoplastic
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Humans
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Neoplasms / diagnosis
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Neoplasms / genetics*
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Oligonucleotide Array Sequence Analysis / standards
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Oligonucleotide Array Sequence Analysis / statistics & numerical data*
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Predictive Value of Tests
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Reproducibility of Results