Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data
Microarray analysis of gene expression is a current topic for the diagnosis and classification of human cancer. A gene expression data microarray consists of an array of thousands of features of which most are irrelevant for classifying patterns of gene expressions. Choosing a minimal subset of feat...
- Autores:
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Católica de Pereira
- Repositorio:
- Repositorio Institucional - RIBUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.ucp.edu.co:10785/10031
- Acceso en línea:
- https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/2014
http://hdl.handle.net/10785/10031
- Palabra clave:
- Rights
- openAccess
- License
- Derechos de autor 2021 Entre Ciencia e Ingeniería
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2022-06-01T19:09:07Z2022-06-01T19:09:07Z2020-12-31https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/201410.31908/19098367.2014http://hdl.handle.net/10785/10031Microarray analysis of gene expression is a current topic for the diagnosis and classification of human cancer. A gene expression data microarray consists of an array of thousands of features of which most are irrelevant for classifying patterns of gene expressions. Choosing a minimal subset of features for classification is a difficult task. In this work, a comparison is made between two multi-objective evolutionary algorithms applied to sets of gene expressions popular in the literature (lymphoma, leukemia and colon). In order to remove the strongly correlated characteristics, a pre-processing stage is performed. An extensive and detailed analysis of the results obtained for the selected multi-objective algorithms is shown.El análisis de microarrays de expresión de genes es un tópico actual para el diagnóstico y clasificación del cáncer humano. Un microarray de datos de expresión de genes consiste en una matriz de miles de características de las cuales la mayoría es irrelevante para clasificar patrones de expresiones de genes. La elección de un subconjunto mínimo de características para clasificación es una tarea dificultosa. En este trabajo, se realiza una comparación entre dos algoritmos evolutivos multiobjetivo aplicados a conjuntos de expresiones de genes populares en la literatura (linfoma, leucemia y colon). Con el objetivo de remover las características con fuerte correlación se realiza una etapa de pre-procesamiento. Se muestra un análisis extenso y detallado de los resultados obtenidos para los algoritmos multiobjetivo seleccionados.application/pdfspaUniversidad Católica de Pereirahttps://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/2014/1863Derechos de autor 2021 Entre Ciencia e Ingenieríahttps://creativecommons.org/licenses/by-nc/4.0/deed.es_EShttps://creativecommons.org/licenses/by-nc/4.0/deed.es_ESinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Entre ciencia e ingeniería; Vol 14 No 28 (2020); 40-45Entre Ciencia e Ingeniería; Vol. 14 Núm. 28 (2020); 40-45Entre ciencia e ingeniería; v. 14 n. 28 (2020); 40-452539-41691909-8367Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer DataAlgoritmos Evolutivos Multiobjetivo aplicados a la Selección de Características en Microarrays de Datos de CáncerArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionDussaut, Julieta SolPonzoni, IgnacioOlivera, Ana CarolinaVidal, Pablo JavierPublication10785/10031oai:repositorio.ucp.edu.co:10785/100312025-01-27 18:59:40.983https://creativecommons.org/licenses/by-nc/4.0/deed.es_ESDerechos de autor 2021 Entre Ciencia e Ingenieríametadata.onlyhttps://repositorio.ucp.edu.coRepositorio Institucional de la Universidad Católica de Pereira - RIBUCbdigital@metabiblioteca.com |
| dc.title.eng.fl_str_mv |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| dc.title.spa.fl_str_mv |
Algoritmos Evolutivos Multiobjetivo aplicados a la Selección de Características en Microarrays de Datos de Cáncer |
| title |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| spellingShingle |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| title_short |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| title_full |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| title_fullStr |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| title_full_unstemmed |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| title_sort |
Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data |
| description |
Microarray analysis of gene expression is a current topic for the diagnosis and classification of human cancer. A gene expression data microarray consists of an array of thousands of features of which most are irrelevant for classifying patterns of gene expressions. Choosing a minimal subset of features for classification is a difficult task. In this work, a comparison is made between two multi-objective evolutionary algorithms applied to sets of gene expressions popular in the literature (lymphoma, leukemia and colon). In order to remove the strongly correlated characteristics, a pre-processing stage is performed. An extensive and detailed analysis of the results obtained for the selected multi-objective algorithms is shown. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-12-31 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-01T19:09:07Z |
| dc.date.available.none.fl_str_mv |
2022-06-01T19:09:07Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
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https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/2014 10.31908/19098367.2014 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10785/10031 |
| url |
https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/2014 http://hdl.handle.net/10785/10031 |
| identifier_str_mv |
10.31908/19098367.2014 |
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spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/2014/1863 |
| dc.rights.spa.fl_str_mv |
Derechos de autor 2021 Entre Ciencia e Ingeniería https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES |
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info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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Derechos de autor 2021 Entre Ciencia e Ingeniería https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Católica de Pereira |
| dc.source.eng.fl_str_mv |
Entre ciencia e ingeniería; Vol 14 No 28 (2020); 40-45 |
| dc.source.spa.fl_str_mv |
Entre Ciencia e Ingeniería; Vol. 14 Núm. 28 (2020); 40-45 |
| dc.source.por.fl_str_mv |
Entre ciencia e ingeniería; v. 14 n. 28 (2020); 40-45 |
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2539-4169 1909-8367 |
| institution |
Universidad Católica de Pereira |
| repository.name.fl_str_mv |
Repositorio Institucional de la Universidad Católica de Pereira - RIBUC |
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bdigital@metabiblioteca.com |
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1834112763563081728 |