|
|
Registros recuperados : 18 | |
3. | | BASSO, M. F.; SILVA, J. C. F.; FAJARDO, T. V. M.; FONTES, E. P. B.; ZERBINI, F. M. A novel, highly divergent ssDNA virus identified in Brazil infecting apple, pear and grapevine. Virus Research, v. 14, n. 210, p. 27-33, July 2015. Biblioteca(s): Embrapa Uva e Vinho. |
| |
4. | | BASSO, M. F.; FAJARDO, T. V. M.; SILVA, J. C. F.; ALFENAS-ZERBINI, P.; ZERBINI, F. M. Association of a novel highly divergent monopartite circular ssDNA virus with chlorotic dwarf and dry branches in apple and pear and potential association with symptoms in grapevine. In: INTERNATIONAL GEMINIVIRUS SYMPOSIUM, 7.; INTERNATIONAL SSDNA COMPARATIVE VIROLOGY WORKSHOP, 5., 2013, Hangzhou. Program and abstracts... [S.l.: s.n., 2013]. p. 56. Biblioteca(s): Embrapa Uva e Vinho. |
| |
5. | | VIEIRA, S. B.; CARVALHO, J. O. P. de; RUSCHEL, A. R.; GOMES, J. M.; SILVA, J. C. F. da. Comportamento de mudas de Cedrela odorata L. (cedro-vermelho) plantadas em clareiras causadas por exploração florestal no município de Paragominas. In: SEMINÁRIO ANUAL DE INICIAÇÃO CIENTÍFICA, 8.; SEMINÁRIO DE PESQUISA DA UFRA, 2., 2010, Belém, PA. Agricultura, pecuária e floresta: integração para sustentabilidade da produção e biodiversidade da Amazônia. Belém, PA: UFRA, 2010. Biblioteca(s): Embrapa Amazônia Oriental. |
| |
6. | | VIEIRA, S. B.; CARVALHO, J. O. P. de; GOMES, J. M.; SILVA, J. C. F. da; RUSCHEL, A. R. Comportamento de mudas de Tabebuia impetiginosa (Mart. ex DC.) Standl. (ipê-roxo) plantadas em clareiras causadas pela exploração de impacto reduzido, em Paragominas, PA. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA PARA O PROGRESSO DA CIÊNCIA, 63., 2011, Goiânia. Cerrado: água, alimento e energia: anais. Goiânia: SBPC, 2011. Biblioteca(s): Embrapa Amazônia Oriental. |
| |
7. | | VIEIRA, S. B.; CARVALHO, J. O. P. de; GOMES, J. M.; SILVA, J. C. F. da; RUSCHEL, A. R. Cedrela odorata L. tem potencial para ser utilizada na silvicultura pós-colheita na Amazônia brasileira? Ciência Florestal, Santa Maria, v. 28, n. 3, p. 1230-1238, jul./set. 2018. Biblioteca(s): Embrapa Amazônia Oriental. |
| |
8. | | QUADROS, L. C. L.; CARVALHO, J. O. P. de; GOMES, J. M.; TAFFAREL, M.; SILVA, J. C. F. Sobrevivência e crescimento de mudas de regeneração natural de Astronium gracile Engl. em clareiras causadas por exploração florestal na Amazônia brasileira. Ciência Florestal, Santa Maria, v. 23, n. 3, p. 411-416, jul./set. 2013. Biblioteca(s): Embrapa Florestas. |
| |
9. | | SANTANA, M. F.; SILVA, J. C. F.; BATISTA, A. D.; RIBEIRO, L. E.; SILVA, G. F. da; ARAÚJO, E. F. de; QUEIROZ, M. V. de. Abundance, distribution and potential impact of transposable elements in the genome of Mycosphaerella fijiensis. BMC Genomics, v. 13, n. 1, p. 1-11, Dec. 2012. Biblioteca(s): Embrapa Amazônia Ocidental. |
| |
10. | | MAR, T. B.; XAVIER, C. A. D.; LIMA, A. T. M.; NOGUEIRA, A. M.; SILVA, J. C. F.; RAMOS-SOBRINHO, R.; LAU, D.; ZERBINI, F. M. Genetic variability and population structure of the New World begomovirus Euphorbia yellow mosaic virus. Journal of General Virology, London, v. 98, n. 6, p. 1537-1551, Jun. 2017. Biblioteca(s): Embrapa Trigo. |
| |
11. | | SILVA, J. C. F. D. DA.; SANTOS, S. A.; SALES, R. L.; CRUZ, L. H. DA.; GARCIA, J. B.; RAVAGLIA, E. Comportamento ingestivo de diferentes categorias de bovinos de corte em pastagens nativas e exóticas durante seca no Pantanal. SIMPÓSIO SOBRE RECURSOS NATURAIS E SOCIOECONÔMICOS DO PANTANAL, 5., 2010, Corumbá, MS. Anais... Corumbá: Embrapa Pantanal: UFMS; Campinas: ICS do Brasil, 2010. 1 CD-ROM SIMPAN 2010. Não Paginado Biblioteca(s): Embrapa Pantanal. |
| |
12. | | SILVA, I. N. da; IKEDA, F. S.; FUJIMORI, I. S. T.; IEKA, L. F.; BAUERMANN, G. S.; CONTESINI, M. E.; BASILIO, E. R.; FAVARO, L.; SILVA, J. C. F. da. Controle químico em pós-emergência de vassourinha-de-botão perenizada em cultivo de milho. In: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 12., 2023. Sinop. Resumos... Brasília, DF: Embrapa, 2023. p. 42. (Embrapa Agrossilvipastoril. Eventos Técnicos & Científicos, 1) Biblioteca(s): Embrapa Agrossilvipastoril. |
| |
13. | | 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. |
| |
14. | | LIMA, L. L.; BALBI, B. P.; MESQUITA, R. O.; SILVA, J. C. F. da; COUTINHO, F. S.; CARMO, F. M. S.; VITAL, C. E.; MEHTA, A.; LOUREIRO, M. E.; FONTES, E. P. B.; BARROS, E. G.; RAMOS, H. J. O. Proteomic and metabolomic analysis of a drought tolerant soybean cultivar from Brazilian savanna. Crop Breeding, Genetics and Genomics, v. 1, article e190022, 2019. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
15. | | IKEDA, F. S.; CAVALIERI, S. D.; FUJIMORI, I. S. T.; SILVA, I. N. da; CONSTESINI, M. E.; BASILIO, E. R.; SILVA, J. C. F. da; FÁVARO, L.; IEKA, L. V. F.; BAUERMANN, G. S. Seletividade inicial de herbicidas aplicados em pós-emergência em cultivares de gergelim. In: SIMPÓSIO DE HERBICIDAS E TECNOLOGIAS ASSOCIADAS, 2., 2023, Jaboticabal, SP. Anais... Jaboticabal, SP: Simpoherbi, 2023. Biblioteca(s): Embrapa Agrossilvipastoril. |
| |
16. | | IKEDA, F. S.; CAVALIERI, S. D.; FUJIMORI, I. S. T.; SILVA, I. N. da; CONTESINI, M. E.; BASILIO, E. R.; SILVA, J. C. F. da; FAVARO, L.; IEKA, L. V. F.; BAUERMANN, G. S. Seletividade inicial de herbicidas em pré-emergência em cultivares de gergelim. In: SIMPÓSIO DE HERBICIDAS E TECNOLOGIAS ASSOCIADAS, 2., 2023, Jaboticabal, SP. Anais... Jaboticabal, SP: Simpoherbi, 2023. Biblioteca(s): Embrapa Agrossilvipastoril. |
| |
17. | | DUARTE, K E. D.; BASSO, M. F.; OLIVEIRA, N. G. de; SILVA, J. C. F. da; GARCIA, B. de O.; DIAS, B. B. A.; CARDOSO, T. B.; NEPOMUCENO, A. L.; KOBAYASHI, A. K.; SANTIAGO, T. R.; SOUZA, W. R. de; MOLINARI, H. B. C. MicroRNAs expression profiles in early responses to different levels of water deficit in Setaria viridis. Physiology and Molecular Biology of Plants, v. 28, n. 8, p. 1607-1624, 2022. Biblioteca(s): Embrapa Agroenergia; Embrapa Soja. |
| |
18. | | PIMENTA, M. R.; SILVA, P. A.; MENDES, G. C.; ALVES, J. R.; CAETANO, H. D. N.; MACHADO, J. P. B.; BRUSTOLINI, O. J. B.; CARPINETTI, P. A.; MELO, B. P.; SILVA, J. C. F.; ROSADO, G. L.; FERREIRA, M. F. S.; DAL-BIANCO, M.; PICOLI, E. A. de T.; ARAGAO, F. J. L.; RAMOS, H. J. O.; FONTES, E. P. B. The Stress-Induced Soybean NAC transcription factor GmNAC81 plays a positive role in developmentally programmed leaf senescence. Plant and Cell Physiology, v. 57, n. 5, p. 1098-1114, 2016. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
| |
Registros recuperados : 18 | |
|
|
| 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
Biblioteca(s): |
Embrapa Gado de Corte. |
Data corrente: |
04/01/2023 |
Data da última atualização: |
04/01/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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. |
Título: |
Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Livestock Science, v. 260, article 104935, 2022. |
ISSN: |
1871-1413 |
DOI: |
https://doi.org/10.1016/j.livsci.2022.104935 |
Idioma: |
Inglês |
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 |
Thesagro: |
Gado de Corte; Gado Nelore; Touro. |
Thesaurus NAL: |
Beef cattle; Bulls; Daughters; Genome; Genomics; Nellore. |
Categoria do assunto: |
-- |
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.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Gado de Corte (CNPGC) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|