Title

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

Publication Date

11-24-2003

Document Type

Article

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.

Publication Title

Genome Biology

Volume

4

Issue

12

DOI

10.1186/gb-2003-4-12-r83

Version

open access

This document is currently not available here.

Find in your library

Share

COinS