A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Autores
Rojas, Matias Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier
Año de publicación
2020
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. hl{This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets.} Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic´ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.
Fil: Rojas, Matias Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
Materia
FEATURE SELECTION
MICROARRAY CLASSIFICATION
CELLULAR GENETIC ALGORITHM
MEMETIC ALGORITHM
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:11336/137065
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature SelectionRojas, Matias GabrielOlivera, Ana CarolinaCarballido, Jessica AndreaVidal, Pablo JavierFEATURE SELECTIONMICROARRAY CLASSIFICATIONCELLULAR GENETIC ALGORITHMMEMETIC ALGORITHMhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. hl{This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets.} Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic´ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.Fil: Rojas, Matias Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaInstitute of Electrical and Electronics Engineers2020-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/137065Rojas, Matias Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier; A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 18; 11; 10-2020; 1874-18831548-0992CONICET DigitalCONICETspainfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/3794info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:11Zoai:ri.conicet.gov.ar:11336/137065instacron: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:34982025-09-03 09:58:11.804CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
title A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
spellingShingle A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
Rojas, Matias Gabriel
FEATURE SELECTION
MICROARRAY CLASSIFICATION
CELLULAR GENETIC ALGORITHM
MEMETIC ALGORITHM
title_short A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
title_full A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
title_fullStr A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
title_full_unstemmed A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
title_sort A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
dc.creator.none.fl_str_mv Rojas, Matias Gabriel
Olivera, Ana Carolina
Carballido, Jessica Andrea
Vidal, Pablo Javier
author Rojas, Matias Gabriel
author_facet Rojas, Matias Gabriel
Olivera, Ana Carolina
Carballido, Jessica Andrea
Vidal, Pablo Javier
author_role author
author2 Olivera, Ana Carolina
Carballido, Jessica Andrea
Vidal, Pablo Javier
author2_role author
author
author
dc.subject.none.fl_str_mv FEATURE SELECTION
MICROARRAY CLASSIFICATION
CELLULAR GENETIC ALGORITHM
MEMETIC ALGORITHM
topic FEATURE SELECTION
MICROARRAY CLASSIFICATION
CELLULAR GENETIC ALGORITHM
MEMETIC ALGORITHM
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. hl{This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets.} Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic´ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.
Fil: Rojas, Matias Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
description Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. hl{This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets.} Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic´ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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/11336/137065
Rojas, Matias Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier; A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 18; 11; 10-2020; 1874-1883
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/137065
identifier_str_mv Rojas, Matias Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier; A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 18; 11; 10-2020; 1874-1883
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/3794
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
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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 13.13397

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