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Support vector machine applications in bioinformatics.

Support vector machine applications in bioinformatics. The support vector machine (SVM) approach represents a data-driven method for solving classification tasks. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. In this review, the theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described. Current developments in techniques related to the SVM approach are reviewed which might become relevant for future functional genomics and chemogenomics projects. In a comparative study, we developed neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. The SVM system was able to correctly classify approximately 90% of the compounds in a cross-validation study yielding a Matthews correlation coefficient of 0.78. This classifier can be used for fast filtering of compound libraries in virtual screening applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied bioinformatics Pubmed

Support vector machine applications in bioinformatics.

Applied bioinformatics , Volume 2 (2): 11 – Jun 10, 2004

Support vector machine applications in bioinformatics.


Abstract

The support vector machine (SVM) approach represents a data-driven method for solving classification tasks. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. In this review, the theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described. Current developments in techniques related to the SVM approach are reviewed which might become relevant for future functional genomics and chemogenomics projects. In a comparative study, we developed neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. The SVM system was able to correctly classify approximately 90% of the compounds in a cross-validation study yielding a Matthews correlation coefficient of 0.78. This classifier can be used for fast filtering of compound libraries in virtual screening applications.

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ISSN
1175-5636
pmid
15130823

Abstract

The support vector machine (SVM) approach represents a data-driven method for solving classification tasks. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. In this review, the theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described. Current developments in techniques related to the SVM approach are reviewed which might become relevant for future functional genomics and chemogenomics projects. In a comparative study, we developed neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. The SVM system was able to correctly classify approximately 90% of the compounds in a cross-validation study yielding a Matthews correlation coefficient of 0.78. This classifier can be used for fast filtering of compound libraries in virtual screening applications.

Journal

Applied bioinformaticsPubmed

Published: Jun 10, 2004

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