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

Genome Biol. 2003;4(12):R83. doi: 10.1186/gb-2003-4-12-r83. Epub 2003 Nov 24.

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.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / genetics
  • Cell Line, Tumor
  • Female
  • Gene Expression Profiling / classification
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Neoplasms / diagnosis
  • Neoplasms / genetics*
  • Oligonucleotide Array Sequence Analysis / standards
  • Oligonucleotide Array Sequence Analysis / statistics & numerical data*
  • Predictive Value of Tests
  • Reproducibility of Results