Support vector machine classification on the web

Authors:
Paul Pavlidis;Ilan Wapinski;William Stafford Noble
Affiliations:
Columbia Genome Center and Department of Biomedical Informatics, Columbia University, 1150 St Nicholas Avenue, New York, NY 10032, USA,;Department of Computer Science, Columbia University, New York, NY 10027, USA;Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA
Venue:
Bioinformatics
Year:
2004

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Abstract

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.