Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data

Autores
Ornella, Leonardo Alfredo; Tapia, Elizabeth
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.
Fil: Ornella, Leonardo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Tapia, Elizabeth. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina
Materia
HETEROTIC GROUPS
MAIZE
SUPERVISED LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/118478
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker dataOrnella, Leonardo AlfredoTapia, ElizabethHETEROTIC GROUPSMAIZESUPERVISED LEARNINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.Fil: Ornella, Leonardo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Tapia, Elizabeth. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; ArgentinaElsevier2010-11info: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/11336/118478Ornella, Leonardo Alfredo; Tapia, Elizabeth; Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data; Elsevier; Computers and Eletronics in Agriculture; 74; 2; 11-2010; 250-2570168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0168169910001572info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2010.08.013info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:31:16Zoai:ri.conicet.gov.ar:11336/118478instacron: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 10:31:17.103CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
title Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
spellingShingle Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
Ornella, Leonardo Alfredo
HETEROTIC GROUPS
MAIZE
SUPERVISED LEARNING
title_short Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
title_full Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
title_fullStr Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
title_full_unstemmed Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
title_sort Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
dc.creator.none.fl_str_mv Ornella, Leonardo Alfredo
Tapia, Elizabeth
author Ornella, Leonardo Alfredo
author_facet Ornella, Leonardo Alfredo
Tapia, Elizabeth
author_role author
author2 Tapia, Elizabeth
author2_role author
dc.subject.none.fl_str_mv HETEROTIC GROUPS
MAIZE
SUPERVISED LEARNING
topic HETEROTIC GROUPS
MAIZE
SUPERVISED LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.
Fil: Ornella, Leonardo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Tapia, Elizabeth. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina
description The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.
publishDate 2010
dc.date.none.fl_str_mv 2010-11
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/11336/118478
Ornella, Leonardo Alfredo; Tapia, Elizabeth; Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data; Elsevier; Computers and Eletronics in Agriculture; 74; 2; 11-2010; 250-257
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/118478
identifier_str_mv Ornella, Leonardo Alfredo; Tapia, Elizabeth; Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data; Elsevier; Computers and Eletronics in Agriculture; 74; 2; 11-2010; 250-257
0168-1699
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://www.sciencedirect.com/science/article/abs/pii/S0168169910001572
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2010.08.013
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
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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