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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
15/10/2020 |
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Data da última atualização: |
15/10/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Scientia Agricola, v. 78, n. 4, e20200021, 2021. |
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DOI: |
http://dx.doi.org/10.1590/1678-992X-2020-0021 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Statistical learning. |
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Thesagro: |
Hemileia Vastatrix. |
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Thesaurus Nal: |
Artificial intelligence; Plant breeding. |
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Categoria do assunto: |
-- |
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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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Registro original: |
Embrapa Café (CNPCa) |
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Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
15/08/2013 |
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Data da última atualização: |
30/03/2023 |
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Autoria: |
MANICA-BERTO, R.; PEGORARO, C.; MISTURA, C. C.; BRESOLIN, A. P. S.; RUFATO, A. De R.; FACHINELLO, J. C. |
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Afiliação: |
ROBERTA MANICA-BERTO, UNIVERSIDADE FEDERAL DE PELOTAS; CAMILA PEGORARO, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO RIO GRANDE DO SUL; CLAUDETE CLARICE MISTURA, UNIVERSIDADE FEDERAL DE PELOTAS; ADRIANA PIRES SOARES BRESOLIN, UNIVERSIDADE FEDRAL DO PAMPA; ANDREA DE ROSSI RUFATO, CNPUV; JOSÉ CARLOS FACHINELLO, UNIVERSIDADE FEDERAL DE PELOTAS. |
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Título: |
Similaridade genética entre cultivares de marmeleiro avaliadas por marcadores AFLP. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 5, p. 568-571, maio 2013. |
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Idioma: |
Português |
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Notas: |
Notas científicas.
Título em português: Genetic similarity between quince cultivars evaluated by AFLP markers. |
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Conteúdo: |
O objetivo deste trabalho foi determinar as relações genéticas de 21 cultivares de marmeleiro com base no marcador ?amplified fragment length polymorphism? (AFLP), para melhoramento e conservação de recursos genéticos da espécie na região sul do Rio Grande do Sul. O DNA das cultivares foi extraído pelo método CTAB, e as reações de AFLP foram realizadas com os iniciadores EcoRI/MseI. Foram identificados dois grupos entre as 21 cultivares de marmeleiro, um com quatro e outro com sete cultivares geneticamente mais relacionadas. As cultivares de marmeleiro apresentam alta variabilidade genética, com máximo de 43% de similaridade. |
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Palavras-Chave: |
Divergência; Germoplasma de marmeleiro; Quince germplasm. |
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Thesagro: |
Cydonia Oblonga. |
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Thesaurus NAL: |
Multicultural diversity. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/87803/1/Similaridade-genetica.pdf
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Marc: |
LEADER 01534naa a2200253 a 4500
001 1964149
005 2023-03-30
008 2013 bl uuuu u00u1 u #d
100 1 $aMANICA-BERTO, R.
245 $aSimilaridade genética entre cultivares de marmeleiro avaliadas por marcadores AFLP.$h[electronic resource]
260 $c2013
500 $aNotas científicas. Título em português: Genetic similarity between quince cultivars evaluated by AFLP markers.
520 $aO objetivo deste trabalho foi determinar as relações genéticas de 21 cultivares de marmeleiro com base no marcador ?amplified fragment length polymorphism? (AFLP), para melhoramento e conservação de recursos genéticos da espécie na região sul do Rio Grande do Sul. O DNA das cultivares foi extraído pelo método CTAB, e as reações de AFLP foram realizadas com os iniciadores EcoRI/MseI. Foram identificados dois grupos entre as 21 cultivares de marmeleiro, um com quatro e outro com sete cultivares geneticamente mais relacionadas. As cultivares de marmeleiro apresentam alta variabilidade genética, com máximo de 43% de similaridade.
650 $aMulticultural diversity
650 $aCydonia Oblonga
653 $aDivergência
653 $aGermoplasma de marmeleiro
653 $aQuince germplasm
700 1 $aPEGORARO, C.
700 1 $aMISTURA, C. C.
700 1 $aBRESOLIN, A. P. S.
700 1 $aRUFATO, A. De R.
700 1 $aFACHINELLO, J. C.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 48, n. 5, p. 568-571, maio 2013.
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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