Modelling Proteolytic Enzymes With Support Vector Machines
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doi:10.2390/biecoll-jib-2011-170
July 07, 2011
September 15, 2011
PubMed ID 21926433
Lionel Morgado, Carlos Pereira, Paula Verissimo and António Dourado
Correspondence should be addressed to:
Lionel Morgado
Center for Informatics and Systems of the University of Coimbra Polo II - University of Coimbra, 3030-290 Coimbra, Portugal
tp.cu.ied@nulllenoil
Abstract
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.