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Bioinformatics Vol. 17 no. 90001 2001
Pages S316-S322
© 2001 Oxford University Press

Molecular classification of multiple tumor types

Chen-Hsiang Yeang , Sridhar Ramaswamy , Pablo Tamayo , Sayan Mukherjee , Ryan M. Rifkin , Michael Angelo , Michael Reich , Eric Lander , Jill Mesirov and Todd Golub

Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA

Received on February 5, 2001 ; revised on April 2, 2001 ; accepted on April 2, 2001

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.

Contact: chyeang{at}mit.edu


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