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Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

Overview of attention for article published in BMC Bioinformatics, September 2015
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Title
Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0731-9
Pubmed ID
Authors

Caroline König, Martha I Cárdenas, Jesús Giraldo, René Alquézar, Alfredo Vellido

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 31%
Student > Bachelor 4 25%
Student > Ph. D. Student 1 6%
Student > Doctoral Student 1 6%
Student > Master 1 6%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Computer Science 5 31%
Biochemistry, Genetics and Molecular Biology 3 19%
Agricultural and Biological Sciences 3 19%
Medicine and Dentistry 2 13%
Neuroscience 2 13%
Other 0 0%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2015.
All research outputs
#7,850,514
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#3,314
of 4,169 outputs
Outputs of similar age
#151,986
of 243,357 outputs
Outputs of similar age from BMC Bioinformatics
#117
of 147 outputs
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So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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