Multiclass classification of microarray data with repeated measurements: application to cancer.
Multiclass classification of microarray data with repeated measurements: application to cancer.
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DOI：
10.1186/gb-2003-4-12-r83
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发表时间：
2003
期刊：
影响因子：
12.3
通讯作者：
Bumgarner RE
中科院分区：
文献类型：
--
作者：
Yeung KY;Bumgarner RE
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. Uncorrelated shrunken centroid and error-weighted, uncorrelated shrunken centroid algorithms have been developed that are applicable to microarray data with any number of classes. 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.
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