Support vector machine classification on the web

Authors: Pavlidis P.¹; Wapinski I.²; Noble W.S.³

Source: Bioinformatics, Volume 20, Number 4, 1 March 2004 , pp. 586-587(2)

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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.

Document Type: Research article

DOI: http://dx.doi.org/10.1093/bioinformatics/btg461

Affiliations: 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

Publication date: 2004-03-01

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The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.