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| Registros recuperados : 7 | |
| 1. |  | 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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| 2. | | LAGE, J. F.; PAULINO, P. V. R.; PEREIRA, L. G. R.; MONNERAT, J. P. I. dos S.; DUARTE, M. de S.; BENEDETI, P. del B.; VALADARES FILHO, S. de C. Glicerina bruta na alimentação de cordeiros em confinamento e seus efeitos sobre o peso e rendimento de cortes comerciais da carcaça. In: CONGRESSO BRASILEIRO DE ZOOTECNIA, 19.; CONGRESSO INTERNACIONAL DE ZOOTECNIA, 11; FÓRUM DE COORDENADORES DE CURSOS DE ZOOTECNIA DAS UNIVERSIDADES BRASILEIRAS, 5.; FÓRUM DE ESTUDANTES DE CURSOS DE ZOOTECNIA DAS UNIVERSIDADES BRASILEIRAS, 5.; REUNIÃO NACIONAL DE ENSINO DE ZOOTECNIA, 15.; FÓRUM DE ENTIDADES DE ZOOTECNISTAS, 22. 2009, Águas de Lindóia. Visão estratégica de cadeias do agronegócio : anais. Pirassununga: Faculdade de Zootecnia e Engenharia de Alimentos: Associação Brasileira de Zootecnistas, 2009. 1 CD-ROM| Biblioteca(s): Embrapa Semiárido. |
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| 3. | | 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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| 4. | | RODRIGUES, A. A. F.; RODRIGUES, M. T.; SILVA, M. M. C. da; ARAÚJO, F. L.; TORRES FILHO, R. de A.; BRANCO, R. H.; PAIVA, V. R.; DUARTE, M. de S. Efeito de dietas com diferentes razões proteína bruta e energia sobre a digestibilidade de cabras de origem leiteira. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 43., 2006, João Pessoa. Produção animal em biomas tropicais: anais dos simpósios. João Pessoa: Sociedade Brasileira de Zootecnia, 2006. 4 f.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 5. | | BRANCO, R. H.; RODRIGUES, M. T.; RODRIGUES, C. A. F.; SILVA, M. M. C. da; MATOS, R. da S.; ARAÚJO, F. L. de; VIANA, V.; DUARTE, M de S. Efeito dos níveis de fibra oriundos da forragem na dieta de cabras em lactação: 1. Consumo de nutrientes e produção de leite. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 42., 2005, Goiânia. A produção animal e o foco no agronegócio: anais. Goiânia: Sociedade Brasileira de Zootecnia, 2005. 6 f. CD ROM.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 6. | | RODRIGUES, C. A. F.; RODRIGUES, M. T.; SILVA, M. M. C. da; LANA, F. de A.; MATOS, R. da S.; DUARTE, M. de S.; SANTANA, R. C. S.; TORRES FILHO, R. de A.; BRANCO, R. H. Desempenho de cabras lactantes alimentadas com rações apresentando diferentes níveis de proteína bruta. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 42., 2005, Goiânia. A produção animal e o foco no agronegócio: anais. Goiânia: Sociedade Brasileira de Zootecnia, 2005. 5 f. CD ROM.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 7. | | 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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| Registros recuperados : 7 | |
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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. |
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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Circulação/Nível: |
A - 1 |
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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: |
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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
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700 1 $aMENEZES, G. R. de O.
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700 1 $aDUARTE, M. de S.
700 1 $aSILVA, F. F. e
773 $tLivestock Science$gv. 260, article 104935, 2022.
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Embrapa Gado de Corte (CNPGC) |
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