Multi-class classification of microarray data with repeated measurements: application to cancer


Ka Yee Yeung and Roger E. Bumgarner


Abstract
Prediction of the diagnostic category of a tissue sample from its expression profile and selection of relevant genes for class prediction have important applications in cancer research. We 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 demonstrated that removing highly correlated genes typically improves classification results using a small set of genes.