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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
Proceedings of the National Academy of Sciences of the United States of America
Vol. 97, No. 1 (Jan. 4, 2000), pp. 262-267
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/121778
Page Count: 6
You can always find the topics here!Topics: Genes, Mathematical vectors, Gene expression, Mathematical expressions, Ribosomal proteins, Machine learning, False positive errors, Learning, Mathematical functions, Hyperplanes
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We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. 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 and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.
Proceedings of the National Academy of Sciences of the United States of America © 2000 National Academy of Sciences