Software
Multiclass classification of microarray data with repeated measurements: application to cancer
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Corresponding authors: Ka Y Yeung kayee@u.washington.edu - Roger E Bumgarner rogerb@uwashington.edu
Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA
Genome Biology 2003, 4:R83 doi:10.1186/gb-2003-4-12-r83
Published: 24 November 2003Abstract
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.