Web of Science: 6 cites, Scopus: 6 cites, Google Scholar: cites,
Label noise in subtype discrimination of class C G protein-coupled receptors : a systematic approach to the analysis of classification errors
König, Caroline (Universitat Politècnica de Catalunya. Departament de Ciencies de la Computació)
Cárdenas, Martha I. (Universitat Politècnica de Catalunya. Departament de Ciencies de la Computació)
Giraldo, Jesús (Universitat Autònoma de Barcelona. Institut de Neurociències)
Alquézar, René (Universitat Politècnica de Catalunya. Departament de Ciencies de la Computació)
Vellido, Alfredo (Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics)

Data: 2015
Resum: Background: The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences. Results: In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories. Conclusions: In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: G Protein-coupled receptors ; Label noise ; Support vector machines ; Phylogenetic trees
Publicat a: BMC Bioinformatics, Vol. 16, N. 314 (September 2015) , p. 1-14, ISSN 1471-2105

DOI: 10.1186/s12859-015-0731-9
PMID: 26415951


14 p, 1.9 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Neurociències (INc)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2017-03-08, darrera modificació el 2018-07-28



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