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Registro Completo |
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
15/10/2020 |
Data da última atualização: |
15/10/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SOUSA, I. C. de; NASCIMENTO, M.; SILVA, G. N.; NASCIMENTO, A. C. C.; CRUZ, C. D.; SILVA, F. F. e; ALMEIDA, D. P. de; PESTANA, K. N.; AZEVEDO, C. F.; ZAMBOLIM, L.; CAIXETA, E. T. |
Afiliação: |
Ithalo Coelho de Sousa, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; Gabi Nunes Silva, Universidade Federal de Rondônia; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Cosme Damião Cruz, Universidade Federal de Viçosa; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Dênia Pires de Almeida, Universidade Federal de Viçosa; Kátia Nogueira Pestana, Embrapa Mandioca e Fruticultura; Camila Ferreira Azevedo, Universidade Federal de Viçosa; Laércio Zambolim, Universidade Federal de Viçosa; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. |
Título: |
Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Scientia Agricola, v. 78, n. 4, e20200021, 2021. |
DOI: |
http://dx.doi.org/10.1590/1678-992X-2020-0021 |
Idioma: |
Inglês |
Conteúdo: |
Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature. MenosGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs)... Mostrar Tudo |
Palavras-Chave: |
Statistical learning. |
Thesagro: |
Hemileia Vastatrix. |
Thesaurus Nal: |
Artificial intelligence; Plant breeding. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216675/1/Sousa-et-al-2020.pdf
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Marc: |
LEADER 02472naa a2200301 a 4500 001 2125524 005 2020-10-15 008 2021 bl uuuu u00u1 u #d 024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2020-0021$2DOI 100 1 $aSOUSA, I. C. de 245 $aGenomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms.$h[electronic resource] 260 $c2021 520 $aGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature. 650 $aArtificial intelligence 650 $aPlant breeding 650 $aHemileia Vastatrix 653 $aStatistical learning 700 1 $aNASCIMENTO, M. 700 1 $aSILVA, G. N. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aCRUZ, C. D. 700 1 $aSILVA, F. F. e 700 1 $aALMEIDA, D. P. de 700 1 $aPESTANA, K. N. 700 1 $aAZEVEDO, C. F. 700 1 $aZAMBOLIM, L. 700 1 $aCAIXETA, E. T. 773 $tScientia Agricola$gv. 78, n. 4, e20200021, 2021.
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Embrapa Café (CNPCa) |
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Registro Completo
Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
Data corrente: |
07/02/2018 |
Data da última atualização: |
20/02/2018 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
OLIVEIRA, M. N. DE; PIMENTEL, T. C.; ESMERINO, E. A.; PRUDÊNCIO, E. S.; SILVA, M. C. DA; GUIMARÃES, J. DE T.; CAPPATO, L. P.; SILVA, H. L. A. DA; BALTHAZAR, C. F.; MORAES, A. E. A. DE; CHAVES, A. C. S. D.; CRUZ, A. G. DA; ZACARCHENCO, P. B. |
Afiliação: |
Maricê Nogueira de Oliveira; Tatiana Colombo Pimentel; Erick Almeida Esmerino; Elane Schwinden Prudêncio; Marcia Cristina da Silva; Jonas de Toledo Guimarães; Leandro Pereira Cappato; Hugo Leandro Azevedo da Silva; Celso Fasura Balthazar; Adriane Elisabete Antunes de Moraes; ANA CAROLINA SAMPAIO DORIA CHAVES, CTAA; Adriano Gomes da Cruz; Patricia Blumer Zacarchenco. |
Título: |
Leites fermentados. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
In: CRUZ, G. A.; ZACARCHENCO, P. B.; OLIVEIRA, C. A. F.; CORASSIN, C. H. (Org.). Processamento de Produtos Lácteos: Queijos, Leites Fermentados, Bebidas Lácteas, Sorvete, Manteiga,Creme de Leite, Doce de Leite, Soro em Pó e Lácteos Funcionais. Rio de Janeiro: Elsevier, 2017. Cap. 6. |
Páginas: |
p. 169-193 |
Série: |
Coleção Lácteos v. 3 |
ISBN: |
978-8535280852 |
Idioma: |
Português |
Palavras-Chave: |
Fermentação do leite; Produtos lácteos. |
Thesagro: |
Leite Fermentado. |
Thesaurus NAL: |
Fermented dairy products. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 01190naa a2200337 a 4500 001 2087263 005 2018-02-20 008 2017 bl uuuu u00u1 u #d 020 $a978-8535280852 100 1 $aOLIVEIRA, M. N. DE 245 $aLeites fermentados.$h[electronic resource] 260 $c2017 300 $ap. 169-193 490 $aColeção Lácteos v. 3 650 $aFermented dairy products 650 $aLeite Fermentado 653 $aFermentação do leite 653 $aProdutos lácteos 700 1 $aPIMENTEL, T. C. 700 1 $aESMERINO, E. A. 700 1 $aPRUDÊNCIO, E. S. 700 1 $aSILVA, M. C. DA 700 1 $aGUIMARÃES, J. DE T. 700 1 $aCAPPATO, L. P. 700 1 $aSILVA, H. L. A. DA 700 1 $aBALTHAZAR, C. F. 700 1 $aMORAES, A. E. A. DE 700 1 $aCHAVES, A. C. S. D. 700 1 $aCRUZ, A. G. DA 700 1 $aZACARCHENCO, P. B. 773 $tIn: CRUZ, G. A.; ZACARCHENCO, P. B.; OLIVEIRA, C. A. F.; CORASSIN, C. H. (Org.). Processamento de Produtos Lácteos: Queijos, Leites Fermentados, Bebidas Lácteas, Sorvete, Manteiga,Creme de Leite, Doce de Leite, Soro em Pó e Lácteos Funcionais. Rio de Janeiro: Elsevier, 2017. Cap. 6.
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