Multiclass Classification of Microarray Data With Repeated Measurements: Application to Cancer

Ka Yee Yeung, University of Washington Tacoma
Roger E. Bumgarner


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.