ReP USP - Resultado da busca

Artigo de periodico
Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize (2023)
Gevartosky, Raysa ; Carvalho, Humberto Fanelli; Costa-Neto, Germano; Montesinos-López, Osval A ; Crossa, José; Fritsche-Neto, Roberto
Source: BMC Plant Biology. Unidade: ESALQ
Subjects: ALOCAÇÃO DE RECURSOS, INTERAÇÃO GENÓTIPO-AMBIENTE, GENÔMICA, MILHO, SELEÇÃO GENÉTICA
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ABNT
GEVARTOSKY, Raysa et al. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC Plant Biology, v. 23, p. 1-20, 2023Tradução . . Disponível em: https://doi.org/10.1186/s12870-022-03975-1. Acesso em: 17 jun. 2024.
APA
Gevartosky, R., Carvalho, H. F., Costa-Neto, G., Montesinos-López, O. A., Crossa, J., & Fritsche-Neto, R. (2023). Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC Plant Biology, 23, 1-20. doi:10.1186/s12870-022-03975-1
NLM
Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize [Internet]. BMC Plant Biology. 2023 ; 23 1-20.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
Vancouver
Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize [Internet]. BMC Plant Biology. 2023 ; 23 1-20.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
Artigo de periodico
Partial least squares enhances genomic prediction of new environments (2022)
Montesinos-López, Osval A; Montesinos-López, Abelardo; Kismiantini; Roman-Gallardo, Armando; Gardner, Keith; Lillemo, Morten; Fritsche-Neto, Roberto ; Crossa, José
Source: Frontiers in Genetics. Unidade: ESALQ
Subjects: GENÔMICA, INFERÊNCIA BAYESIANA, INTERAÇÃO GENÓTIPO-AMBIENTE, MELHORAMENTO GENÉTICO VEGETAL, MILHO, MÍNIMOS QUADRADOS, SELEÇÃO GENÉTICA, TRIGO
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ABNT
MONTESINOS-LÓPEZ, Osval A et al. Partial least squares enhances genomic prediction of new environments. Frontiers in Genetics, v. 13, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3389/fgene.2022.920689. Acesso em: 17 jun. 2024.
APA
Montesinos-López, O. A., Montesinos-López, A., Kismiantini,, Roman-Gallardo, A., Gardner, K., Lillemo, M., et al. (2022). Partial least squares enhances genomic prediction of new environments. Frontiers in Genetics, 13, 1-17. doi:10.3389/fgene.2022.920689
NLM
Montesinos-López OA, Montesinos-López A, Kismiantini, Roman-Gallardo A, Gardner K, Lillemo M, Fritsche-Neto R, Crossa J. Partial least squares enhances genomic prediction of new environments [Internet]. Frontiers in Genetics. 2022 ; 13 1-17.[citado 2024 jun. 17 ] Available from: https://doi.org/10.3389/fgene.2022.920689
Vancouver
Montesinos-López OA, Montesinos-López A, Kismiantini, Roman-Gallardo A, Gardner K, Lillemo M, Fritsche-Neto R, Crossa J. Partial least squares enhances genomic prediction of new environments [Internet]. Frontiers in Genetics. 2022 ; 13 1-17.[citado 2024 jun. 17 ] Available from: https://doi.org/10.3389/fgene.2022.920689
Artigo de periodico
Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize (2021)
Costa-Neto, Germano; Crossa, Jose; Fritsche-Neto, Roberto
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: MILHO, GENÔMICA, INTERAÇÃO GENÓTIPO-AMBIENTE, GENÉTICA QUANTITATIVA, FENÓTIPOS, CLIMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA-NETO, Germano e CROSSA, Jose e FRITSCHE-NETO, Roberto. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize. Frontiers in Plant Science, v. 12, p. 1-21, 2021Tradução . . Disponível em: https://doi.org/10.3389/fpls.2021.717552. Acesso em: 17 jun. 2024.
APA
Costa-Neto, G., Crossa, J., & Fritsche-Neto, R. (2021). Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize. Frontiers in Plant Science, 12, 1-21. doi:10.3389/fpls.2021.717552
NLM
Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize [Internet]. Frontiers in Plant Science. 2021 ; 12 1-21.[citado 2024 jun. 17 ] Available from: https://doi.org/10.3389/fpls.2021.717552
Vancouver
Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize [Internet]. Frontiers in Plant Science. 2021 ; 12 1-21.[citado 2024 jun. 17 ] Available from: https://doi.org/10.3389/fpls.2021.717552
Artigo de periodico
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2020)
Costa Neto, Germano Martins Ferreira ; Fritsche Neto, Roberto ; Crossa, José
Source: Heredity. Unidade: ESALQ
Subjects: GENOMAS, GENÔMICA, INTERAÇÃO GENÓTIPO-AMBIENTE, MILHO, MODELOS MATEMÁTICOS
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ABNT
COSTA NETO, Germano Martins Ferreira e FRITSCHE NETO, Roberto e CROSSA, José. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, p. 1-15, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41437-020-00353-1. Acesso em: 17 jun. 2024.
APA
Costa Neto, G. M. F., Fritsche Neto, R., & Crossa, J. (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, 1-15. doi:10.1038/s41437-020-00353-1
NLM
Costa Neto GMF, Fritsche Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials [Internet]. Heredity. 2020 ; 1-15.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1038/s41437-020-00353-1
Vancouver
Costa Neto GMF, Fritsche Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials [Internet]. Heredity. 2020 ; 1-15.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1038/s41437-020-00353-1
Artigo de periodico
BGGE: A New package for genomic-enabled prediction incorporating genotype × environment interaction models (2018)
Granato, Italo; Cuevas, Jaime; Luna, Francisco; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Fritsche-Neto, Roberto
Source: G3: Genes, Genomes, Genetics. Unidade: ESALQ
Subjects: GENÔMICA, INFERÊNCIA BAYESIANA, INTERAÇÃO GENÓTIPO-AMBIENTE, MELHORAMENTO GENÉTICO VEGETAL, REGRESSÃO LINEAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GRANATO, Italo et al. BGGE: A New package for genomic-enabled prediction incorporating genotype × environment interaction models. G3: Genes, Genomes, Genetics, p. 1-22, 2018Tradução . . Disponível em: https://doi.org/10.1534/g3.118.200435. Acesso em: 17 jun. 2024.
APA
Granato, I., Cuevas, J., Luna, F., Crossa, J., Montesinos-López, O. A., Burgueño, J., & Fritsche-Neto, R. (2018). BGGE: A New package for genomic-enabled prediction incorporating genotype × environment interaction models. G3: Genes, Genomes, Genetics, 1-22. doi:10.1534/g3.118.200435
NLM
Granato I, Cuevas J, Luna F, Crossa J, Montesinos-López OA, Burgueño J, Fritsche-Neto R. BGGE: A New package for genomic-enabled prediction incorporating genotype × environment interaction models [Internet]. G3: Genes, Genomes, Genetics. 2018 ; 1-22.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.118.200435
Vancouver
Granato I, Cuevas J, Luna F, Crossa J, Montesinos-López OA, Burgueño J, Fritsche-Neto R. BGGE: A New package for genomic-enabled prediction incorporating genotype × environment interaction models [Internet]. G3: Genes, Genomes, Genetics. 2018 ; 1-22.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.118.200435
Artigo de periodico
Genomic-enabled prediction Kernel models with Random intercepts for multi-environment trials (2018)
Cuevas, J; Granato, I; Fritsche-Neto, Roberto; Montesinos-Lopez, OA; Burgueno, Juan; Bandeira, Massaine Bandeira e; Crossa, J
Source: G3: Genes, Genomes, Genetics. Unidade: ESALQ
Subjects: INTERAÇÃO GENÓTIPO-AMBIENTE, MARCADOR MOLECULAR, SELEÇÃO GENÉTICA, GENÔMICA, MODELOS MATEMÁTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUEVAS, J et al. Genomic-enabled prediction Kernel models with Random intercepts for multi-environment trials. G3: Genes, Genomes, Genetics, v. 8, n. 4, p. 1347-1365, 2018Tradução . . Disponível em: https://doi.org/10.1534/g3.117.300454. Acesso em: 17 jun. 2024.
APA
Cuevas, J., Granato, I., Fritsche-Neto, R., Montesinos-Lopez, O. A., Burgueno, J., Bandeira, M. B. e, & Crossa, J. (2018). Genomic-enabled prediction Kernel models with Random intercepts for multi-environment trials. G3: Genes, Genomes, Genetics, 8( 4), 1347-1365. doi:10.1534/g3.117.300454
NLM
Cuevas J, Granato I, Fritsche-Neto R, Montesinos-Lopez OA, Burgueno J, Bandeira MB e, Crossa J. Genomic-enabled prediction Kernel models with Random intercepts for multi-environment trials [Internet]. G3: Genes, Genomes, Genetics. 2018 ; 8( 4): 1347-1365.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.117.300454
Vancouver
Cuevas J, Granato I, Fritsche-Neto R, Montesinos-Lopez OA, Burgueno J, Bandeira MB e, Crossa J. Genomic-enabled prediction Kernel models with Random intercepts for multi-environment trials [Internet]. G3: Genes, Genomes, Genetics. 2018 ; 8( 4): 1347-1365.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.117.300454
Artigo de periodico
Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction (2017)
Souza, Massaine Bandeira e; Cuevas, Jaime; Couto, Evellyn Giselly de Oliveira; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose
Source: G3: Genes, Genomes, Genetics. Unidade: ESALQ
Subjects: INTERAÇÃO GENÓTIPO-AMBIENTE, GENÔMICA, MILHO, MODELOS MATEMÁTICOS, SELEÇÃO GENÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Massaine Bandeira e et al. Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction. G3: Genes, Genomes, Genetics, v. 7, n. 6, p. 1995-2014, 2017Tradução . . Disponível em: https://doi.org/10.1534/g3.117.042341. Acesso em: 17 jun. 2024.
APA
Souza, M. B. e, Cuevas, J., Couto, E. G. de O., Pérez-Rodríguez, P., Jarquín, D., Fritsche-Neto, R., et al. (2017). Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction. G3: Genes, Genomes, Genetics, 7( 6), 1995-2014. doi:10.1534/g3.117.042341
NLM
Souza MB e, Cuevas J, Couto EG de O, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, Burgueño J, Crossa J. Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction [Internet]. G3: Genes, Genomes, Genetics. 2017 ; 7( 6): 1995-2014.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.117.042341
Vancouver
Souza MB e, Cuevas J, Couto EG de O, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, Burgueño J, Crossa J. Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction [Internet]. G3: Genes, Genomes, Genetics. 2017 ; 7( 6): 1995-2014.[citado 2024 jun. 17 ] Available from: https://doi.org/10.1534/g3.117.042341

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