Modelling Proteolytic Enzymes With Support Vector Machines

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 sol...

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Bibliographic Details
Published inJournal of integrative bioinformatics Vol. 8; no. 3; pp. 1 - 15
Main Authors Morgado, Lionel, Pereira, Carlos, Veríssimo, Paula, Dourado, António
Format Journal Article
LanguageEnglish
Published De Gruyter 01.12.2011
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Summary: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.
ISSN:1613-4516
1613-4516
DOI:10.1515/jib-2011-170