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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...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2010
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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). |
format | Text |
id | pubmed-2930845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT saracomersinan gopredgomolecularfunctionpredictionbycombinedclassifiers AT atalayvolkan gopredgomolecularfunctionpredictionbycombinedclassifiers AT cetinatalayrengul gopredgomolecularfunctionpredictionbycombinedclassifiers |