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
Publisher Policy
open access
Open Access Status
OA Journal
Recommended Citation
Yeung, Ka Yee and Bumgarner, Roger E., "Multiclass Classification of Microarray Data With Repeated Measurements: Application to Cancer" (2003). School of Engineering and Technology Publications. 310.
https://digitalcommons.tacoma.uw.edu/tech_pub/310
