Inference of functional relations in predicted protein networks with a machine learning approach

[Background] Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, an...

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Autores: García-Jiménez, Beatriz, Juan, David, Ezkurdia, Iakes, Andrés-León, Eduardo, Valencia, Alfonso
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2010
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/412973
Acceso en línea:http://hdl.handle.net/10261/412973
https://api.elsevier.com/content/abstract/scopus_id/77956368930
Access Level:acceso abierto
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spelling Inference of functional relations in predicted protein networks with a machine learning approachGarcía-Jiménez, BeatrizJuan, DavidEzkurdia, IakesAndrés-León, EduardoValencia, Alfonso[Background] Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome[Methodology/Principal Findings] We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/.[Conclusions] We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project ‘‘Functions for gene sets’’ from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublic Library of ScienceBiosapiensMinisterio de Educación y Ciencia (España)202620262010info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/412973https://api.elsevier.com/content/abstract/scopus_id/77956368930reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MEC//CSD2007-00050info:eu-repo/grantAgreement/MEC//BIO2007-66855García-Jiménez, Beatriz; Juan, David; Ezkurdia, Iakes; Andrés-León, Eduardo; Valencia, Alfonso; 2010; Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach [Dataset]; Figshare; https://doi.org/10.1371/journal.pone.0009969https://doi.org/10.1371/journal.pone.0009969Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4129732026-05-22T06:33:51Z
dc.title.none.fl_str_mv Inference of functional relations in predicted protein networks with a machine learning approach
title Inference of functional relations in predicted protein networks with a machine learning approach
spellingShingle Inference of functional relations in predicted protein networks with a machine learning approach
García-Jiménez, Beatriz
title_short Inference of functional relations in predicted protein networks with a machine learning approach
title_full Inference of functional relations in predicted protein networks with a machine learning approach
title_fullStr Inference of functional relations in predicted protein networks with a machine learning approach
title_full_unstemmed Inference of functional relations in predicted protein networks with a machine learning approach
title_sort Inference of functional relations in predicted protein networks with a machine learning approach
dc.creator.none.fl_str_mv García-Jiménez, Beatriz
Juan, David
Ezkurdia, Iakes
Andrés-León, Eduardo
Valencia, Alfonso
author García-Jiménez, Beatriz
author_facet García-Jiménez, Beatriz
Juan, David
Ezkurdia, Iakes
Andrés-León, Eduardo
Valencia, Alfonso
author_role author
author2 Juan, David
Ezkurdia, Iakes
Andrés-León, Eduardo
Valencia, Alfonso
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Biosapiens
Ministerio de Educación y Ciencia (España)
description [Background] Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome
publishDate 2010
dc.date.none.fl_str_mv 2010
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/412973
https://api.elsevier.com/content/abstract/scopus_id/77956368930
url http://hdl.handle.net/10261/412973
https://api.elsevier.com/content/abstract/scopus_id/77956368930
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/MEC//CSD2007-00050
info:eu-repo/grantAgreement/MEC//BIO2007-66855
García-Jiménez, Beatriz; Juan, David; Ezkurdia, Iakes; Andrés-León, Eduardo; Valencia, Alfonso; 2010; Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach [Dataset]; Figshare; https://doi.org/10.1371/journal.pone.0009969
https://doi.org/10.1371/journal.pone.0009969
No
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
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