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

Caroline König, René Alquézar, Alfredo Vellido, Jesús Giraldo

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)254
JournalJournal of integrative bioinformatics
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Jan 2014

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