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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
04/01/2023 |
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Data da última atualização: |
04/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
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. |
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Afiliação: |
TALITA ESTEFANI ZUNINO SANTANA, UNIVERSIDADE FEDERAL DE VIÇOSA; JOSE CLEYDSON F. SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIZ OTAVIO CAMPOS DA SILVA, CNPGC; AMANDA BOTELHO ALVARENGA, PURDUE UNIVERSITY; GILBERTO ROMEIRO DE OLIVEIRA MENEZE, CNPGC; ROBERTO AUGUSTO DE A TORRES JUNIOR, CNPGC; MARCIO DE SOUZA DUARTE, UNIVERSITY GUELPH; FABYANO FONSECA E SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Livestock Science, v. 260, article 104935, 2022. |
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ISSN: |
1871-1413 |
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DOI: |
https://doi.org/10.1016/j.livsci.2022.104935 |
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Idioma: |
Inglês |
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Conteúdo: |
Stayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle. MenosStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number ... Mostrar Tudo |
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Thesagro: |
Gado de Corte; Gado Nelore; Touro. |
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Thesaurus Nal: |
Beef cattle; Bulls; Daughters; Genome; Genomics; Nellore. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02992naa a2200337 a 4500
001 2150623
005 2023-01-04
008 2022 bl uuuu u00u1 u #d
022 $a1871-1413
024 7 $ahttps://doi.org/10.1016/j.livsci.2022.104935$2DOI
100 1 $aSANTANA, T. E. Z.
245 $aGenome-enabled classification of stayability in Nellore cattle under a machine learning framework.$h[electronic resource]
260 $c2022
520 $aStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle.
650 $aBeef cattle
650 $aBulls
650 $aDaughters
650 $aGenome
650 $aGenomics
650 $aNellore
650 $aGado de Corte
650 $aGado Nelore
650 $aTouro
700 1 $aSILVA, J. C. F.
700 1 $aSILVA, L. O. C. da
700 1 $aALVARENGA, A. B.
700 1 $aMENEZES, G. R. de O.
700 1 $aTORRES JUNIOR, R. A. de A.
700 1 $aDUARTE, M. de S.
700 1 $aSILVA, F. F. e
773 $tLivestock Science$gv. 260, article 104935, 2022.
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Registro original: |
Embrapa Gado de Corte (CNPGC) |
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| Registros recuperados : 319 | |
| 7. |  | BARICHELLO, F.; ALENCAR, M. M. de; TORRES JÚNIOR, R. A. de A. Estudo preliminar de análises de parâmetros genéticos em dados simulados de escores visuais com diferentes distribuições, por meio de inferência bayesiana. SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 7., 2008, São Carlos, SP. Anais... São Carlos, SP: SBMA: Embrapa Pecuária Sudeste, 2008. 4 p. 1 CD-ROM. CNPGC.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 8. | | BARICHELLO, F.; ALENCAR, M. M. de; TORRES JUNIOR, R. A. de A. Efeito dos parâmetros do amostrador de Gibbs sobre o valor genético de escores visuais simulados. In: JORNADA CIENTÍFICA DA EMBRAPA GADO DE CORTE, 4., 2008, Campo Grande, MS. Anais [da]... Campo Grande, MS: Embrapa Gado de Corte, 2008. 1 CD-ROM. Editores técnicos Valdemir Antônio Laura, Paulo Henrique Nogueira Biscola. 1 p. 1 CD-ROM.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 9. | | BARICHELLO, F.; ALENCAR, M. M. de; TORRES JUNIOR, R. A. de A. Genetic analysis of visual score data with different distributions and genetic parameters using linear and nonlinear models. Journal of Animal Science, v. 88, (E-Suppl. 2), p. 699, 2010; Journal of Dairy Science, v. 93, (E-Suppl. 1), p. 699, 2010.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 12. | | AMARAL, T. B.; MEDEIROS, S. R.; AURIEMO, A. J. B.; TORRES JÚNIOR, R. A. de A. Intervalo parto-primeiro cio, taxa de retorno ao cio, taxa de prenhez e colesterol plasmático de vacas Nelore primíparas submetidas a dois níveis de energia na dieta pós-parto. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 43., 2006, João Pessoa. Produção animal em biomas tropicais: anais. João Pessoa: Sociedade Brasileira de Zootecnia: UFPB, 2006. 4 p. 1 CD-ROM. CNPGC.| Biblioteca(s): Embrapa Gado de Corte. |
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| 14. | | SILVA, R. A.; MEDEIROS, S. R. de; REIS, S. F.; TORRES JUNIOR, R. A. de A. Teste de avaliação visual para taxa de acabamento de novilhas de corte. In: JORNADA CIENTÍFICA DA EMBRAPA GADO DE CORTE,5., 2009, Campo Grande, MS. [Anais da ...]. Campo Grande, MS: Embrapa Gado de Corte, 2009. 1 p.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 18. | | MENEZES, G. R. de O.; TORRES JUNIOR, R. A. de A. Uso do cruzamento em gado de corte: o sucesso passa pela seleção. In: MENEZES, G. R. de O.; NOBRE, P. R. C.; TORRES JUNIOR, R. A. de A.; GONDO, A.; SILVA, L. O. C. da; SILVA, L. N. (Ed.). Sumário Senepol 2015: Sumário de touros Senepol Geneplus-Embrapa. Brasília, DF: Embrapa, 2015. 1 p.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 20. | | MARTIN NIETO, L.; SILVA, L. O. C. da; ROSA, A. do N.; TORRES JÚNIOR, R. A. de A.; GONDO, A. Análise da curva de crescimento do perímetro escrotal de touros Canchim em diferentes sistemas de criação. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 41., 2004, Campo Grande, MS. A produção animal e a segurança alimentar: anais dos simpósios e dos resumos. Campo Grande, MS: Sociedade Brasileira de Zootecnia: Embrapa Gado de Corte, 2004. 4 p. MELH 122. 1 CD-ROM. CNPGC.| Biblioteca(s): Embrapa Gado de Corte. |
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| Registros recuperados : 319 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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