An evolutionary approach for searching metabolic pathways

Autores
Gerard, Matias Fernando; Stegmayer, Georgina; Milone, Diego Humberto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies.
Fil: Gerard, Matias Fernando. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Materia
Search Strategies
Evolutionary Algorithms
Metabolic Pathways
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14570
Acceder
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network_name_str CONICET Digital (CONICET)
spelling An evolutionary approach for searching metabolic pathwaysGerard, Matias FernandoStegmayer, GeorginaMilone, Diego HumbertoSearch StrategiesEvolutionary AlgorithmsMetabolic Pathwayshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies.Fil: Gerard, Matias Fernando. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; ArgentinaFil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; ArgentinaElsevier2013-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14570Gerard, Matias Fernando; Stegmayer, Georgina; Milone, Diego Humberto; An evolutionary approach for searching metabolic pathways; Elsevier; Computers In Biology And Medicine; 43; 11; 11-2013; 1704-17120010-4825enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2013.08.017info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0010482513002321info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:41Zoai:ri.conicet.gov.ar:11336/14570instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:41:41.839CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An evolutionary approach for searching metabolic pathways
title An evolutionary approach for searching metabolic pathways
spellingShingle An evolutionary approach for searching metabolic pathways
Gerard, Matias Fernando
Search Strategies
Evolutionary Algorithms
Metabolic Pathways
title_short An evolutionary approach for searching metabolic pathways
title_full An evolutionary approach for searching metabolic pathways
title_fullStr An evolutionary approach for searching metabolic pathways
title_full_unstemmed An evolutionary approach for searching metabolic pathways
title_sort An evolutionary approach for searching metabolic pathways
dc.creator.none.fl_str_mv Gerard, Matias Fernando
Stegmayer, Georgina
Milone, Diego Humberto
author Gerard, Matias Fernando
author_facet Gerard, Matias Fernando
Stegmayer, Georgina
Milone, Diego Humberto
author_role author
author2 Stegmayer, Georgina
Milone, Diego Humberto
author2_role author
author
dc.subject.none.fl_str_mv Search Strategies
Evolutionary Algorithms
Metabolic Pathways
topic Search Strategies
Evolutionary Algorithms
Metabolic Pathways
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies.
Fil: Gerard, Matias Fernando. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina
description Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies.
publishDate 2013
dc.date.none.fl_str_mv 2013-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/14570
Gerard, Matias Fernando; Stegmayer, Georgina; Milone, Diego Humberto; An evolutionary approach for searching metabolic pathways; Elsevier; Computers In Biology And Medicine; 43; 11; 11-2013; 1704-1712
0010-4825
url http://hdl.handle.net/11336/14570
identifier_str_mv Gerard, Matias Fernando; Stegmayer, Georgina; Milone, Diego Humberto; An evolutionary approach for searching metabolic pathways; Elsevier; Computers In Biology And Medicine; 43; 11; 11-2013; 1704-1712
0010-4825
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2013.08.017
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0010482513002321
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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