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- W2123496056 endingPage "R83" @default.
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- W2123496056 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." @default.
- W2123496056 created "2016-06-24" @default.
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- W2123496056 date "2003-01-01" @default.
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- W2123496056 doi "https://doi.org/10.1186/gb-2003-4-12-r83" @default.
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