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

Ka Yee Yeung and Roger E Bumgarner

Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA

author email corresponding author email

Genome Biology 2003, 4:R83doi:10.1186/gb-2003-4-12-r83


Published:	24 November 2003

Subject areas: Bioinformatics, Genome studies

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.