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Summary: Knowledge-based analysis of microarray gene
expression data by using support vector machines
Michael P. S. Brown*, William Noble Grundy
, David Lin*, Nello Cristianini§
, Charles Walsh Sugnet¶
, Terrence S. Furey*,
Manuel Ares, Jr.¶
, and David Haussler*
*Department of Computer Science and ¶Center for Molecular Biology of RNA, Department of Biology, University of California, Santa Cruz, Santa Cruz, CA
95064; Department of Computer Science, Columbia University, New York, NY 10025; §Department of Engineering Mathematics, University of Bristol, Bristol
BS8 1TR, United Kingdom
Edited by David Botstein, Stanford University School of Medicine, Stanford, CA, and approved November 15, 1999 (received for review August 31, 1999)
We introduce a method of functionally classifying genes by using
gene expression data from DNA microarray hybridization experi-
ments. The method is based on the theory of support vector
machines (SVMs). SVMs are considered a supervised computer
learning method because they exploit prior knowledge of gene
function to identify unknown genes of similar function from
expression data. SVMs avoid several problems associated with
unsupervised clustering methods, such as hierarchical clustering
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