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.