Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)

Autores
Stegmayer, Georgina; Gerard, Matias Fernando; Milone, Diego Humberto
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gerard, Matias Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Data mining
systems biology
clustering
validation
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:11336/189789
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)Stegmayer, GeorginaGerard, Matias FernandoMilone, Diego HumbertoData miningsystems biologyclusteringvalidationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gerard, Matias Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitute of Electrical and Electronics Engineers2012-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/189789Stegmayer, Georgina; Gerard, Matias Fernando; Milone, Diego Humberto; Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629); Institute of Electrical and Electronics Engineers; Ieee Computational Intelligence Magazine; 7; 4; 10-2012; 22-341556-603XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/6331731info:eu-repo/semantics/altIdentifier/doi/10.1109/MCI.2012.2215122info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:43:49Zoai:ri.conicet.gov.ar:11336/189789instacron: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:34982025-10-15 14:43:49.876CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
title Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
spellingShingle Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
Stegmayer, Georgina
Data mining
systems biology
clustering
validation
title_short Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
title_full Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
title_fullStr Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
title_full_unstemmed Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
title_sort Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629)
dc.creator.none.fl_str_mv Stegmayer, Georgina
Gerard, Matias Fernando
Milone, Diego Humberto
author Stegmayer, Georgina
author_facet Stegmayer, Georgina
Gerard, Matias Fernando
Milone, Diego Humberto
author_role author
author2 Gerard, Matias Fernando
Milone, Diego Humberto
author2_role author
author
dc.subject.none.fl_str_mv Data mining
systems biology
clustering
validation
topic Data mining
systems biology
clustering
validation
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gerard, Matias Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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/11336/189789
Stegmayer, Georgina; Gerard, Matias Fernando; Milone, Diego Humberto; Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629); Institute of Electrical and Electronics Engineers; Ieee Computational Intelligence Magazine; 7; 4; 10-2012; 22-34
1556-603X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/189789
identifier_str_mv Stegmayer, Georgina; Gerard, Matias Fernando; Milone, Diego Humberto; Data mining over biological datasets: an integrated approach based on computational intelligence (IF2012 4.629); Institute of Electrical and Electronics Engineers; Ieee Computational Intelligence Magazine; 7; 4; 10-2012; 22-34
1556-603X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/6331731
info:eu-repo/semantics/altIdentifier/doi/10.1109/MCI.2012.2215122
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
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 13.22299

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