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Modelling Proteolytic Enzymes With Support Vector Machines

Morgado, Lionel ; Pereira, Carlos ; Verissimo, Paula ; Dourado, António

Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)

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The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.

Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Modelling Proteolytic Enzymes With Support Vector Machines. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 8(3), 2011

Online-Journal: http://journal.imbio.de/article.php?aid=170
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2011/5186

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 Latest update: 15 Feb 2011
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