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| Registros recuperados : 145 | |
| 65. |  | SILVA, F. R. F. da; CARVALHO, G. A.; CRUZ, P. G. da; SALMAN, A. K. D.; SCHMITT, E. Degradabilidade do capim-marandu manejado sob pastejo por vacas suplementadas ou não com óleo de soja. PUBVET, Londrina, v. 12, n.4, a69, p.1-7, Abr. 2018.| Biblioteca(s): Embrapa Rondônia. |
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| 67. | | BUENO, A. de F.; CARVALHO, G. A.; SANTOS, A. C. dos; SOSA-GOMEZ, D. R.; SILVA, D. M. da. Pesticide selectivity to natural enemies: challenges and constraints for research and field recommendation. Ciência Rural, Santa Maria, v. 47, n. 6, e20160829, 2017. 10 p.| Biblioteca(s): Embrapa Soja. |
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| 69. | | CARVALHO, G. A. de; SALMAN, A. K. D.; CIPRIANI, H. N.; FARIA, F. R.; SANTIAGO, E.; HALFEN, J.; SCHIMITT, E. Comportamento em pastejo e temperatura interna de vacas lactantes Gir x Holandês suplementadas com óleo de soja. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 52., 2015, Belo Horizonte. Zootecnia: otimizando recursos e potencialidades: anais. Brasília, DF: Sociedade Brasileira de Zootecnia, 2015. 1 CD-ROM.| Biblioteca(s): Embrapa Rondônia. |
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| 70. | | VEIT, H. M.; CARVALHO, G. A. de; SALMAN, A. K. D.; CIPRIANI, H. N.; HALFEN, J.; SCHMITT, E. Comportamento em pastejo e temperatura interna de vacas lactantes Gir x Holandês suplementadas com óleo de soja. In: ENCONTRO DE INICIAÇÃO À PESQUISA DA EMBRAPA RONDÔNIA, 6.; ENCONTRO DE PÓS-GRADUAÇÃO, 2015, Porto Velho, RO. Anais... Porto Velho: Embrapa Rondônia, 2015. p. 30.| Biblioteca(s): Embrapa Rondônia. |
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| 72. | | SOUZA, J. R.; CARVALHO, G. A.; MOURA, A. P. de; COUTO, M. H. G.; MAIA, J. B. Impact of insecticides used to control Spodoptera frugiperda (J.E. Smith) in corn on survival, sex ratio, and reproduction of Trichogramma pretiosum Riley offspring. Chilean Journal of Agricultural Research, v. 73, n. 2, p. 122-127, Apr./Jun. 2013.| Biblioteca(s): Embrapa Hortaliças. |
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| 74. | | REDOAN, A. C. M.; CARVALHO, G. A.; CRUZ, I.; FIGUEIREDO, M. de L. C.; SILVA, R. B. da. Physiological selectivity of insecticides to adult of Doru luteipes (Scudder, 1876) (Dermaptera: Forficulidae). Revista Ciência Agronômica, Fortaleza, v. 44, n. 4, p. 842-850, out./dez. 2013.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 75. | | SCHAFFERT, R. E.; SILVA, L. A.; ALVES, V. M. C.; CARVALHO, G. A.; MAGALHAES, J. V. D. The effect of the AltSB gene on root growth in nutrient solution of isogenic sorghum hybrids. In: INTERNATIONAL PLANT NUTRITION COLLOQUIUM, 16., 2009, Sacramento, California. Proceedings... Davis: University of California, 2009.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 76. | | CARVALHO, G. A.; REIS, P. R.; MORAES, J. C.; FUINI, L. C.; ROCHA, L. C. D.; GOUSSAIN, M. M. Efeitos de alguns inseticidas utilizados na cultura do tomateiro (Lycopersicon esculentum Mill.) a Trichogramma pretiosum Riley, 1879 (Himenoptera: Trichogrammatidae.) Ciência e Agrotecnologia, Lavras, v. 26, n. 1, p. 1160-1663, jan./fev.2002.| Biblioteca(s): Embrapa Hortaliças. |
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| 77. | | GUIMARÃES, L. F. R.; COSTA, M. A.; GONTIJO, P. A.; CARVALHO, G. A.; MOURA, A. P. de. Efeitos colaterais de produtos fitossanitárioas utilizados no controle de Helicoverpa armigera sobre Trichogramma pretiosum (Hymenoptera: Trichogrammatidae). In: JORNADA CIENTÍFICA DA EMBRAPA HORTALIÇAS, 4., 2014, Brasília, DF. Resumos... Brasília: Embrapa Hortaliças, 2014. Resumo 10.| Biblioteca(s): Embrapa Hortaliças. |
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| 78. | | ZACHÉ, R. R. da C.; CARVALHO, G. A.; ZACHÉ, B.; CARVALHO, C. F.; PEREIRA, R. R. da C. Efeitos de fungicidas sobre os aspectos biológicos de Aphis gossypii Glover, 1877 (Hemiptera: Aphididae) em plantas de pepino. Ciência e Agrotecnologia, Lavras, v. 34, n. 6, p. 1431-1438, nov./dez. 2010.| Biblioteca(s): Embrapa Hortaliças. |
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| 79. | | SCHLOSSER, E. R. M.; SALMAN, A. K. D.; CRUZ, P. G. da; CARVALHO, G. A. de; SOUZA, E. C. de. Comparação de metodologias para monitoramento da frequência respiratória de novilhas leiteiras em diferentes horários do dia. In: ENCONTRO DE INICIAÇÃO À PESQUISA DA EMBRAPA RONDÔNIA, 10.; ENCONTRO DE PÓS-GRADUAÇÃO DA EMBRAPA RONDÔNIA, 5., 2019, Porto Velho. Anais... Porto Velho, RO: Embrapa Rondônia, 2019. p. 17.| Biblioteca(s): Embrapa Rondônia. |
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| 80. | | REDOAN, A. C.; CARVALHO, G. A de; CRUZ, I.; FIGUEIREDO, M. de L. C.; SILVA, R. B. da. Efeito de inseticidas usados na cultura do milho (Zea mays L.) sobre ninfas e adultos de Doru luteipes (Scudder) (Dermaptera: forficulidae) em semicampo. Revista Brasileira de Milho e Sorgo, Sete Lagoas, v. 9, n. 3, p. 223-235, 2010.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 145 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Cerrados. |
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Data corrente: |
05/05/2021 |
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Data da última atualização: |
14/05/2021 |
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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: |
LOPES, F. B.; MAGNABOSCO, C. de U.; PASSAFARO, T. L.; BRUNES, L. C.; COSTA, M. F. O. e; EIFERT, E. da C.; NARCISO, M. G.; ROSA, G. J. M.; LOBO, R. B.; BALDI, F. |
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Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF; EDUARDO DA COSTA EIFERT, CPAC; MARCELO GONCALVES NARCISO, CNPAF. |
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Título: |
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 5, 2020. |
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Páginas: |
p. 438-448 |
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Idioma: |
Inglês |
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Conteúdo: |
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle MenosThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relative... Mostrar Tudo |
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Palavras-Chave: |
Bayesian regression models; Carne macia; Deep learning; Genomic selection; Maciez da carne. |
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Thesagro: |
Carne; Gado de Corte; Genética Animal; Seleção Genética. |
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Thesaurus NAL: |
Animal breeding; Zebu. |
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Categoria do assunto: |
--
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223045/1/Magnabosco-Improving-genomic-prediction-accuracy-for-meat-tenderness-in.pdf
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Marc: |
LEADER 02672naa a2200373 a 4500
001 2131678
005 2021-05-14
008 2020 bl uuuu u00u1 u #d
100 1 $aLOPES, F. B.
245 $aImproving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.$h[electronic resource]
260 $c2020
300 $ap. 438-448
520 $aThe goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10?6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree‐based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle
650 $aAnimal breeding
650 $aZebu
650 $aCarne
650 $aGado de Corte
650 $aGenética Animal
650 $aSeleção Genética
653 $aBayesian regression models
653 $aCarne macia
653 $aDeep learning
653 $aGenomic selection
653 $aMaciez da carne
700 1 $aMAGNABOSCO, C. de U.
700 1 $aPASSAFARO, T. L.
700 1 $aBRUNES, L. C.
700 1 $aCOSTA, M. F. O. e
700 1 $aEIFERT, E. da C.
700 1 $aNARCISO, M. G.
700 1 $aROSA, G. J. M.
700 1 $aLOBO, R. B.
700 1 $aBALDI, F.
773 $tJournal of Animal Breeding and Genetics$gv. 137, n. 5, 2020.
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