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GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best...

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Detalles Bibliográficos
Autores principales: Saraç, Ömer Sinan, Atalay, Volkan, Cetin-Atalay, Rengul
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930845/
https://www.ncbi.nlm.nih.gov/pubmed/20824206
http://dx.doi.org/10.1371/journal.pone.0012382
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author Saraç, Ömer Sinan
Atalay, Volkan
Cetin-Atalay, Rengul
author_facet Saraç, Ömer Sinan
Atalay, Volkan
Cetin-Atalay, Rengul
author_sort Saraç, Ömer Sinan
collection PubMed
description Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).
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spelling pubmed-29308452010-09-03 GOPred: GO Molecular Function Prediction by Combined Classifiers Saraç, Ömer Sinan Atalay, Volkan Cetin-Atalay, Rengul PLoS One Research Article Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred). Public Library of Science 2010-08-31 /pmc/articles/PMC2930845/ /pubmed/20824206 http://dx.doi.org/10.1371/journal.pone.0012382 Text en Saraç et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Saraç, Ömer Sinan
Atalay, Volkan
Cetin-Atalay, Rengul
GOPred: GO Molecular Function Prediction by Combined Classifiers
title GOPred: GO Molecular Function Prediction by Combined Classifiers
title_full GOPred: GO Molecular Function Prediction by Combined Classifiers
title_fullStr GOPred: GO Molecular Function Prediction by Combined Classifiers
title_full_unstemmed GOPred: GO Molecular Function Prediction by Combined Classifiers
title_short GOPred: GO Molecular Function Prediction by Combined Classifiers
title_sort gopred: go molecular function prediction by combined classifiers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930845/
https://www.ncbi.nlm.nih.gov/pubmed/20824206
http://dx.doi.org/10.1371/journal.pone.0012382
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