Multiclass classification of microarray data with repeated measurements: application to cancer.

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.

journal_name

Genome Biol

journal_title

Genome biology

authors

Yeung KY,Bumgarner RE

doi

10.1186/gb-2003-4-12-r83

keywords:

subject

Has Abstract

pub_date

2003-01-01 00:00:00

pages

R83

issue

12

eissn

1474-7596

issn

1474-760X

pii

gb-2003-4-12-r83

journal_volume

4

pub_type

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