title: 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.
authors.affiliation: Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu
authors.forename: Ka Yee
authors.initials: KY
authors.lastname: Yeung
authors.forename: Roger E
authors.initials: RE
authors.lastname: Bumgarner
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D045744:Cell Line, Tumor; D005260:Female; D020869:Gene Expression Profiling / Q000145:classification / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000592:standards / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results