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Bioinformatics Advance Access originally published online on January 22, 2004
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Bioinformatics 20(4) © Oxford University Press 2004; all rights reserved.

Applications Note

Support vector machine classification on the web

Paul Pavlidis 1,*, Ilan Wapinski 2,{dagger} and William Stafford Noble 3

1 Columbia Genome Center and Department of Biomedical Informatics, Columbia University, 1150 St Nicholas Avenue, New York, NY 10032, USA, 2 Department of Computer Science, Columbia University, New York, NY 10027, USA and 3 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA

Received on August 13, 2003 ; revised on October 3, 2003 ; accepted on October 6, 2003
Advance Access Publication January 22, 2004

Summary: The support vector machine (SVM) learning algorithm has been widely applied in bioinformatics. We have developed a simple web interface to our implementation of the SVM algorithm, called Gist. This interface allows novice or occasional users to apply a sophisticated machine learning algorithm easily to their data. More advanced users can download the software and source code for local installation. The availability of these tools will permit more widespread application of this powerful learning algorithm in bioinformatics.

Availability: Web interface at svm.sdsc.edu. Binaries and source code at microarray.cpmc.columbia.edu/gist.

Contact: pp175{at}columbia.edu

* To whom correspondence should be addressed.

{dagger} Present address: Division of Engineering and Applied Sciences and Center for Genome Research, Harvard University, Cambridge, MA 02138, USA


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