Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee

Detalhes bibliográficos
Autor(a) principal: Silva, Gabi Nunes
Data de Publicação: 2017
Outros Autores: Nascimento, Moyses, Sant'Anna, Isabela de Castro, Cruz, Cosme Damião, Caixeta, Eveline Teixeira, Carneiro, Pedro Crescêncio Souza, Rosado, Renato Domiciano Silva, Pestana, Katia Nogueira, Almeida, Dênia Pires de, Oliveira, Marciane da Silva
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/23873
Resumo: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
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spelling Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffeeRedes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábicaCoffea arabica; Hemileia vastatrix; artificial intelligence; molecular markers; predictionCoffea arabica; Hemileia vastatrix; inteligência artificial; marcadores moleculares; prediçãoThe objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.O objetivo deste trabalho foi avaliar o uso de redes neurais artificiais em comparação à modelagem por meio de modelos lineares generalizados na predição de resistência à ferrugem em café arábica (Coffea  arabica). Foram utilizados 245 indivíduos provenientes de uma população F2, oriundos da autofecundação do híbrido F1 H511-1, resultante do cruzamento da cultivar suscetível Catuaí Amarelo IAC 64 (UFV 2148-57) e do genitor resistente Híbrido de Timor (UFV 443-03). Os 245 indivíduos foram genotipados com 137 marcadores. Realizaram-se análises com redes neurais artificiais e com modelos lineares generalizados sob o enfoque bayesiano. As redes neurais identificaram quatro marcadores importantes pertencentes a grupos de ligação que foram recentemente mapeados, enquanto o modelo generalizado bayesiano identificou somente dois marcadores pertencentes a esses grupos. Foram observadas taxas de erro de predição inferiores (1,60%) para predizer a resistência à ferrugem em café arábica, quando foram utilizadas as redes neurais artificiais em vez de modelos lineares generalizados sob o enfoque bayesiano (2,4%). Os resultados mostraram que as redes neurais artificiais são uma abordagem promissora para predizer a resistência à ferrugem em café arábica.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)Fundação de Pesquisa do Estado de Minas Gerais (Fapemig)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Silva, Gabi NunesNascimento, MoysesSant'Anna, Isabela de CastroCruz, Cosme DamiãoCaixeta, Eveline TeixeiraCarneiro, Pedro Crescêncio SouzaRosado, Renato Domiciano SilvaPestana, Katia NogueiraAlmeida, Dênia Pires deOliveira, Marciane da Silva2017-04-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/23873Pesquisa Agropecuaria Brasileira; v.52, n.3, mar. 2017; 186-193Pesquisa Agropecuária Brasileira; v.52, n.3, mar. 2017; 186-1931678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAenghttps://seer.sct.embrapa.br/index.php/pab/article/view/23873/13727https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/23873/15812Direitos autorais 2017 Pesquisa Agropecuária Brasileirainfo:eu-repo/semantics/openAccess2017-07-07T18:19:19Zoai:ojs.seer.sct.embrapa.br:article/23873Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2017-07-07T18:19:19Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica
title Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
spellingShingle Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
Silva, Gabi Nunes
Coffea arabica; Hemileia vastatrix; artificial intelligence; molecular markers; prediction
Coffea arabica; Hemileia vastatrix; inteligência artificial; marcadores moleculares; predição
title_short Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
title_full Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
title_fullStr Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
title_full_unstemmed Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
title_sort Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
author Silva, Gabi Nunes
author_facet Silva, Gabi Nunes
Nascimento, Moyses
Sant'Anna, Isabela de Castro
Cruz, Cosme Damião
Caixeta, Eveline Teixeira
Carneiro, Pedro Crescêncio Souza
Rosado, Renato Domiciano Silva
Pestana, Katia Nogueira
Almeida, Dênia Pires de
Oliveira, Marciane da Silva
author_role author
author2 Nascimento, Moyses
Sant'Anna, Isabela de Castro
Cruz, Cosme Damião
Caixeta, Eveline Teixeira
Carneiro, Pedro Crescêncio Souza
Rosado, Renato Domiciano Silva
Pestana, Katia Nogueira
Almeida, Dênia Pires de
Oliveira, Marciane da Silva
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)
Fundação de Pesquisa do Estado de Minas Gerais (Fapemig)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.contributor.author.fl_str_mv Silva, Gabi Nunes
Nascimento, Moyses
Sant'Anna, Isabela de Castro
Cruz, Cosme Damião
Caixeta, Eveline Teixeira
Carneiro, Pedro Crescêncio Souza
Rosado, Renato Domiciano Silva
Pestana, Katia Nogueira
Almeida, Dênia Pires de
Oliveira, Marciane da Silva
dc.subject.por.fl_str_mv Coffea arabica; Hemileia vastatrix; artificial intelligence; molecular markers; prediction
Coffea arabica; Hemileia vastatrix; inteligência artificial; marcadores moleculares; predição
topic Coffea arabica; Hemileia vastatrix; artificial intelligence; molecular markers; prediction
Coffea arabica; Hemileia vastatrix; inteligência artificial; marcadores moleculares; predição
description The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
publishDate 2017
dc.date.none.fl_str_mv 2017-04-20
dc.type.none.fl_str_mv
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/23873
url https://seer.sct.embrapa.br/index.php/pab/article/view/23873
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/23873/13727
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/23873/15812
dc.rights.driver.fl_str_mv Direitos autorais 2017 Pesquisa Agropecuária Brasileira
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Direitos autorais 2017 Pesquisa Agropecuária Brasileira
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
dc.source.none.fl_str_mv Pesquisa Agropecuaria Brasileira; v.52, n.3, mar. 2017; 186-193
Pesquisa Agropecuária Brasileira; v.52, n.3, mar. 2017; 186-193
1678-3921
0100-104x
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repository.name.fl_str_mv Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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