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

Ka Yee Yeung; Roger E Bumgarner

doi:10.1186/gb-2003-4-12-r83

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

Ka Yee Yeung·Roger E Bumgarner

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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.

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Algorithms Breast Neoplasms Cell Line, Tumor Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Neoplasms Oligonucleotide Array Sequence Analysis Predictive Value of Tests Reproducibility of Results Journal Article Research Support, U.S. Gov't, P.H.S.

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Ka Yee Yeung, Roger E Bumgarner. 2003-11-24. Multiclass classification of microarray data with repeated measurements: application to cancer.. https://doi.org/10.1186/gb-2003-4-12-r83

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