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Genome Biol. 2003;4(12):R83. Epub 2003 Nov 24.

Multiclass classification of microarray data with repeated measurements: application to cancer.

Author information

  • 1Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu

Erratum in

  • Genome Biol. 2005;6(13):405-405.4.


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.

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