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| Registros recuperados : 125 | |
| 101. |  | EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I. F.; ALVES, R. S.; SILVA, F. F. e; RESENDE, M. D. V. de; SILVA, F. L. da; BHERING, L. L. Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach. Crop Breeding and Applied Biotechnology, v. 21, n. 1, e359721111, 2021.| Biblioteca(s): Embrapa Café. |
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| 102. | | RESENDE, R. T.; SOARES, A. A. V.; FORRESTER, D. I.; MARCATTI, G. E.; SANTOS, A. R. dos; TAKAHASHI, E. K.; SILVA, F. F. e; GRATTAPAGLIA, D.; RESENDE, M. D. V. de; LEITE, H. G. Environmental uniformity, site quality and tree competition interact to determine stand productivity of clonal Eucalyptus. Forest Ecology and Management, v. 410, p. 76-83, Feb. 2018.| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| 103. | | RAMOS, P. V. B.; SILVA, L. O. C. da; PEREZ, B. da C.; TORRES JUNIOR, R. A. de A.; MENEZES, G. R. de O.; ALVARENGA, L. C.; TORRES FILHO, R. de A.; DUARTE, M. de S.; SILVA, F. F. e. Stayability and consecutive rebreeding ability associated to carcass and growth traits in Brazilian Nellore cattle: a Bayesian framework. Livestock Science, v. 245, 2021.| Biblioteca(s): Embrapa Gado de Corte. |
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| 104. | | FARIA, G. M. P. de; PEREIRA, C. S.; GRANATO, I. S. C.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SASALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Controle de qualidade de dados genotípicos para estudos genômicos em clones de eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 84-87.| Biblioteca(s): Embrapa Florestas. |
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| 105. | | ALMEIDA FILHO, J. E. de; AZEVEDO, C. F.; MARINHO, C. D.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Parametrizações em marcadores dominantes para seleção genômica ampla em eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 13-16.| Biblioteca(s): Embrapa Florestas. |
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| 106. | | ALVES, R. S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; ROCHA, J. R. A. S. C.; NUNES, A. C. P.; CARNEIRO, A. P. S.; SANTOS, G. A. dos. Optimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients. Tree Genetics & Genomes, v. 16, n. 2, p. 1-8, 2020.| Biblioteca(s): Embrapa Café. |
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| 107. | | MIRANDA, T. L. R.; RESENDE, M. D. V. de; AZEVEDO, C. F.; NUNES, A. C. P.; TAKAHASHI, E. K.; SIMIQUELI, G. F.; SILVA, F. F. e; ALVES, R. S. Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection. Scientia Agricola, v. 79, n. 6, p. 1-7, 2022.| Biblioteca(s): Embrapa Café. |
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| 108. | | RAMOS, P. B. B.; MENEZES, G. R. de O.; SILVA, D. A. DA; LOURENCO, D.; SANTIAGO, G. G.; TORRES JUNIOR, R. A. de A.; SILVA, F. F. E; LOPES, P. S.; VERONEZA, R. Genomic analysis of feed efficiency traits in beef cattle using random regression models. Journal Animal Breeding and Genetics, 2023. Online ahead of print.| Biblioteca(s): Embrapa Gado de Corte. |
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| 109. | | RAMOS, P. V. B.; SILVA, F. F. e; SILVA, L. O. C. da; SANTIAGO, G. G.; MENEZES, G. R. de O.; VIANA, J. M. S.; TORRES JUNIOR, R. A. de A.; GONDO, A.; LUIZ F. BRITO. Genomic evaluation for novel stayability traits in Nellore cattle. Reproduction in Domestic Animals, v. 55, n. 3, p. 266-273, March 2020.| Biblioteca(s): Embrapa Gado de Corte. |
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| 110. | | SILVA, F. F. e; RESENDE, M. D. V. de; ROCHA, G. S.; DUARTE, D. A. S.; LOPES, P. S.; BRUSTOLIN, O. J. B.; THUS, S.; VIANA, J. M. S.; GUIMARÃES, S. E. F. Genomic growth curves of an outbred pig population. Genetics and Molecular Biology, v. 36, n. 4, p. 520-527, 2013.| Biblioteca(s): Embrapa Florestas. |
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| 111. | | ALMEIDA FILHO, J. E. de A.; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JÚNIOR, M. F. R. de. Genomic prediction of additive and non-additive effects using genetic markers and pedigrees. G3: Genes, Genomes, Genetics, v. 9, p. 2739-2748, Aug. 2019.| Biblioteca(s): Embrapa Florestas. |
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| 112. | | TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M. Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021.| Biblioteca(s): Embrapa Gado de Leite. |
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| 113. | | 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. Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agricola, v. 78, n. 4, e20200021, 2021.| Biblioteca(s): Embrapa Café. |
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| 114. | | SILVA, F. F. e; ZAMBRANO, M. F. B.; VARONA, L.; GLÓRIA, L. S.; LOPES, P. S.; SILVA, M. V. G. B.; ARBEX, W. A.; LÁZARO, S. F.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F. Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. Scientia Agricola, v. 74, n. 1, 2017. 7 P.| Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
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| 115. | | SANTANA, T. E. Z.; SILVA, J. C. F.; SILVA, L. O. C. da; ALVARENGA, A. B.; MENEZES, G. R. de O.; TORRES JUNIOR, R. A. de A.; DUARTE, M. de S.; SILVA, F. F. e. Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. Livestock Science, v. 260, article 104935, 2022.| Biblioteca(s): Embrapa Gado de Corte. |
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| 116. | | RESENDE, R. T.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; MELO, L. C.; PEREIRA, H. S.; SOUZA, T. L. P. O. de; VALDISSER, P. A. M. R.; BRONDANI, C.; VIANELLO, R. P. Genome-wide association and regional heritability mapping of plant architecture, lodging and productivity in Phaseolus vulgaris. G3: Genes, Genomes, Genetics, v. 8, p. 2841-2854, Aug. 2018.| Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Florestas. |
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| 117. | | DUARTE, D. A. S.; FORTES, M. R. S.; DUARTE, M. de S.; GUIMARÃES, S. E. F.; VERARDO, L. L.; VERONEZE, R.; RIBEIRO, A. M. F.; LOPES, P. S.; RESENDE, M. D. V. de; SILVA, F. F. e. Genome-wide association studies, meta-analyses and derived gene network for meat quality and carcass traits in pigs. Animal Production Science, v. 58, n. 6, p. 1100-1008, May 2018.| Biblioteca(s): Embrapa Florestas. |
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| 118. | | CARRARA, E. R.; PEIXOTO, M. G. C. D.; VERONEZE, R.; SILVA, F. F. e; RAMOS, P. V. B.; BRUNELI, F. A. T.; ZADRA, L. E. F.; VENTURA, H. T.; JOSAHKIAN, L. A.; LOPES, P. S. Genetic study of quantitative traits supports the use of Guzerá as dual-purpose cattle. Animal Bioscience, v. 35, n. 7, p. 955-963, 2022.| Biblioteca(s): Embrapa Gado de Leite. |
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| 119. | | FARIA, G. M. P. de; PEREIRA, C. S.; GRANATO, I. S. C.; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SASALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Controle de qualidade de dados genotípicos para estudos genômicos em clones de eucalipto. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 1068-1071| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 120. | | GRANATO, I. S. C.; MARINHO, C. D.; ALMEIDA FILHO, J. E. de; RESENDE, M. D. V. de; SILVA, F. F. e; FERREIRA, K. C. Z.; ROSSE, L. N.; SANSALONI, C. P.; PETROLI, C. D.; GRATTAPAGLIA, D. Seleção de marcadores para os métodos RR-BLUP e BLASSO na seleção genômica ampla. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 285-288.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 125 | |
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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
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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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