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| Registros recuperados : 370 | |
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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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Circulação/Nível: |
A - 1 |
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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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