title: Modelling proteolytic enzymes with Support Vector Machines.
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.
authors.affiliation: Center for Informatics and Systems of the University of Coimbra Polo II - University of Coimbra, 3030-290 Coimbra, Portugal. lionel@dei.uc.pt
authors.forename: Lionel
authors.initials: L
authors.lastname: Morgado
authors.forename: Carlos
authors.initials: C
authors.lastname: Pereira
authors.forename: Paula
authors.initials: P
authors.lastname: Veríssimo
authors.forename: António
authors.initials: A
authors.lastname: Dourado
mesh_terms: D030562:Databases, Protein; D008962:Models, Theoretical; D010447:Peptide Hydrolases / Q000235:genetics*; D060388:Support Vector Machine
chemical_list: D010447:Peptide Hydrolases