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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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| Acesso ao texto completo restrito à biblioteca da Embrapa Clima Temperado. Para informações adicionais entre em contato com cpact.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Clima Temperado. |
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Data corrente: |
11/07/2017 |
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Data da última atualização: |
11/07/2017 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
FREITAS, T. C. de; GUARINO, E. de S. G.; SOUSA, L. P. de; MIURA, A. K.; LUCAS, R. R.; ESPINDOLA, V. S.; RIBEIRO, J. F.; GOMES, G. C. |
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Afiliação: |
Thales Castilhos de Freitas; ERNESTINO DE SOUZA GOMES GUARINO, CPAF-Acre; LETICIA PENNO DERETI, CNPF; ADALBERTO KOITI MIURA, CPACT; Renata Rodrigues Lucas, Faculdade de Agronomia Eliseu Maciel, Universidade Federal de Pelotas; Vinicius Schmalfuss Espindola, Faculdade de Agronomia Eliseu Maciel, Universidade Federal de Pelotas; JOSE FELIPE RIBEIRO, CPAC; Gustavo Crizel Gomes, Bolsista de Pós-Doutorado FAPEG, convênio Embrapa Clima Temperado/CORSAN. |
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Título: |
Lista de espécies arborescentes e arbóreas para restauração ecológica de formações florestais no bioma Pampa. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: CONGRESSO NACIONAL DE BOTÂNICA, 67., 2016, Vitória. Conectando diversidades, revelando o desconhecido: anais. Vitória: SBB, 2016. |
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Páginas: |
2 p. |
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Idioma: |
Português |
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Conteúdo: |
Este trabalho teve como objetivo sistematizar informações sobre espécies arbóreas e arborescentes nativas indicadas para restauração ecológica de formações florestais no bioma Pampa. |
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Palavras-Chave: |
Bioma Pampa; Espécies nativas; Restauração florestal; Rio Grande do Sul. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01071naa a2200265 a 4500
001 2072471
005 2017-07-11
008 2016 bl uuuu u00u1 u #d
100 1 $aFREITAS, T. C. de
245 $aLista de espécies arborescentes e arbóreas para restauração ecológica de formações florestais no bioma Pampa.$h[electronic resource]
260 $c2016
300 $a2 p.
520 $aEste trabalho teve como objetivo sistematizar informações sobre espécies arbóreas e arborescentes nativas indicadas para restauração ecológica de formações florestais no bioma Pampa.
653 $aBioma Pampa
653 $aEspécies nativas
653 $aRestauração florestal
653 $aRio Grande do Sul
700 1 $aGUARINO, E. de S. G.
700 1 $aSOUSA, L. P. de
700 1 $aMIURA, A. K.
700 1 $aLUCAS, R. R.
700 1 $aESPINDOLA, V. S.
700 1 $aRIBEIRO, J. F.
700 1 $aGOMES, G. C.
773 $tIn: CONGRESSO NACIONAL DE BOTÂNICA, 67., 2016, Vitória. Conectando diversidades, revelando o desconhecido: anais. Vitória: SBB, 2016.
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Embrapa Clima Temperado (CPACT) |
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