title: Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns.
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.
authors.affiliation: Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, BarcelonaTech, 08034, Barcelona, Spain.
authors.forename: Caroline
authors.initials: C
authors.lastname: König
authors.affiliation: Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, BarcelonaTech, 08034, Barcelona, Spain.
authors.forename: René
authors.initials: R
authors.lastname: Alquézar
authors.affiliation: Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, BarcelonaTech, 08034, Barcelona, Spain.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Vellido
authors.affiliation: Institut de Neurociències, Unitat de Bioestadística, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain.
authors.forename: Jesús
authors.initials: J
authors.lastname: Giraldo
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods*; D010802:Phylogeny; D043562:Receptors, G-Protein-Coupled / Q000145:classification*; D060388:Support Vector Machine
chemical_list: D043562:Receptors, G-Protein-Coupled