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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
Artigo de periodico
Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids (2022)
Galli, Giovanni ; Sabadin, F; Yassue, Rafael Massahiro ; Galves, C; Carvalho, H. F; Crossa, J; Montesinos-López, O. A; Fritsche-Neto, Roberto
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, GENÔMICA, HIBRIDAÇÃO VEGETAL, MILHO, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GALLI, Giovanni et al. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, v. 13, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.3389/fpls.2022.845524. Acesso em: 08 out. 2024.
APA
Galli, G., Sabadin, F., Yassue, R. M., Galves, C., Carvalho, H. F., Crossa, J., et al. (2022). Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids. Frontiers in Plant Science, 13, 1-13. doi:10.3389/fpls.2022.845524
NLM
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2024 out. 08 ] Available from: https://doi.org/10.3389/fpls.2022.845524
Vancouver
Galli G, Sabadin F, Yassue RM, Galves C, Carvalho HF, Crossa J, Montesinos-López OA, Fritsche-Neto R. Automated machine learning: a case study of genomic “image-based” prediction in maize hybrids [Internet]. Frontiers in Plant Science. 2022 ; 13 1-13.[citado 2024 out. 08 ] Available from: https://doi.org/10.3389/fpls.2022.845524
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.3389/fgene.2022.920689
Artigo de periodico
The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data (2021)
Crossa, Jose; Fritsche-Neto, Roberto ; Montesinos-Lopez, Osval A; Costa-Neto, Germano ; Dreisigacker, Susanne; Montesinos-Lopez, Abelardo; Bentley, Alison R
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: MELHORAMENTO GENÉTICO VEGETAL, GENÔMICA, FENÓTIPOS, MUDANÇA CLIMÁTICA, CULTIVO DE PLANTAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CROSSA, Jose et al. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, v. 12, p. 1-6, 2021Tradução . . Disponível em: https://doi.org/10.3389/fpls.2021.651480. Acesso em: 08 out. 2024.
APA
Crossa, J., Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A., & Bentley, A. R. (2021). The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, 12, 1-6. doi:10.3389/fpls.2021.651480
NLM
Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data [Internet]. Frontiers in Plant Science. 2021 ; 12 1-6.[citado 2024 out. 08 ] Available from: https://doi.org/10.3389/fpls.2021.651480
Vancouver
Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data [Internet]. Frontiers in Plant Science. 2021 ; 12 1-6.[citado 2024 out. 08 ] Available from: https://doi.org/10.3389/fpls.2021.651480

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