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Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns

By Caroline König, René Alquezar, Alfredo Vellido and Jesús Giraldo

Abstract

G protein-coupled receptors (GPCRs) are a large and heterogeneous superfamily of receptors that are key cell players for their role as extracellular signal transmitters. Class C GPCRs, in particular, are of great interest in pharmacology. The lack of knowledge about their full 3-D structure prompts the use of their primary amino acid sequences for the construction of robust classifiers, capable of discriminating their different subtypes. In this paper, we investigate the use of feature selection techniques to build Support Vector Machine (SVM)-based classification models from selected receptor subsequences described as n-grams. We show that this approach to classification is useful for finding class C GPCR subtype-specific motifs

Topics: Data processing, computer science, computer systems
Publisher: Faculties. Faculty of Technology, Research Groups in Informatics
Year: 2014
OAI identifier: oai:biecoll.ub.uni-bielefeld.de:5346

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