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 Bioljournal_title
Genome biologyauthors
Yeung KY,Bumgarner REdoi
10.1186/gb-2003-4-12-r83keywords:
subject
Has Abstractpub_date
2003-01-01 00:00:00pages
R83issue
12eissn
1474-7596issn
1474-760Xpii
gb-2003-4-12-r83journal_volume
4pub_type
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