Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

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
Stable URL: http://www.jstor.org/stable/121778
Page Count: 6
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines
Preview not available

Abstract

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.

Page Thumbnails

  • Thumbnail: Page 
262
    262
  • Thumbnail: Page 
263
    263
  • Thumbnail: Page 
264
    264
  • Thumbnail: Page 
265
    265
  • Thumbnail: Page 
266
    266
  • Thumbnail: Page 
267
    267