Molecular classification of multiple tumor types.


Journal Article

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Full Text

Cited Authors

  • Yeang, CH; Ramaswamy, S; Tamayo, P; Mukherjee, S; Rifkin, RM; Angelo, M; Reich, M; Lander, E; Mesirov, J; Golub, T

Published Date

  • January 2001

Published In

Volume / Issue

  • 17 Suppl 1 /

Start / End Page

  • S316 - S322

PubMed ID

  • 11473023

Pubmed Central ID

  • 11473023

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/17.suppl_1.s316


  • eng