Person: Costa-Neto, G.
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Costa-Neto
First Name
G.
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Costa-Neto, G.
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0000-0003-1137-6786 12 results
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- Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software(Elsevier Ltd., 2025) Crossa, J.; Martini, J.W.R.; Vitale, P.; Pérez-Rodríguez, P.; Costa-Neto, G.; Fritsche-Neto, R.; Runcie, D.E.; Cuevas, J.; Toledo, F.H.; Huihui Li; De Vita, P.; Gerard, G.S.; Dreisigacker, S.; Crespo Herrera, L.A.; Saint Pierre, C.; Bentley, A.R.; Lillemo, M.; Ortiz, R.; Montesinos-Lopez, O.A.; Montesinos-López, A.
Publication - Machine learning algorithms translate big data into predictive breeding accuracy(Elsevier Ltd., 2025) Crossa, J.; Montesinos-Lopez, O.A.; Costa-Neto, G.; Vitale, P.; Martini, J.W.R.; Runcie, D.E.; Fritsche-Neto, R.; Montesinos-Lopez, A.; Pérez-Rodríguez, P.; Gerard, G.S.; Dreisigacker, S.; Crespo Herrera, L.A.; Saint Pierre, C.; Lillemo, M.; Cuevas, J.; Bentley, A.R.; Ortiz, R.
Publication - Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones(Springer Verlag, 2024) Callister, A.N.; Costa-Neto, G.; Bradshaw, B.; Elms, S.; Crossa, J.; Brawner, J.T.
Publication - Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical maize(BioMed Central, 2023) Gevartosky, R.; Fanelli Carvalho, H.; Costa-Neto, G.; Montesinos-Lopez, O.A.; Crossa, J.; Fritsche-Neto, R.
Publication - Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding(Wiley, 2023) Fradgley, N.; Bacon, J.; Bentley, A.R.; Costa-Neto, G.; Cottrell, A.; Crossa, J.; Cuevas, J.; Kerton, M.; Pope, E.; Swarbreck, S.M.; Gardner, K.A.
Publication - Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data(Genetics Society of America, 2023) Costa-Neto, G.; Crespo Herrera, L.A.; Fradgley, N.; Gardner, K.A.; Bentley, A.R.; Dreisigacker, S.; Fritsche-Neto, R.; Montesinos-Lopez, O.A.; Crossa, J.
Publication - Chapter 9. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction(Humana Press Inc., 2022) Crossa, J.; Montesinos-Lopez, O.A.; Pérez-Rodríguez, P.; Costa-Neto, G.; Fritsche-Neto, R.; Ortiz, R.; Martini, J.W.R.; Lillemo, M.; Montesinos-López, A.; Jarquin, D.; Breseghello, F.; Cuevas, J.; Rincent, R.
Publication - Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize(Frontiers, 2021) Costa-Neto, G.; Crossa, J.; Fritsche-Neto, R.
Publication - Optimizing genomic-enabled prediction in small-scale maize hybrid breeding programs: a roadmap review(Frontiers, 2021) Fritsche-Neto, R.; Galli, G.; Borges, K.L.R.; Costa-Neto, G.; Alves, F.C.; Sabadin, F.; Lyra, D.H.; Morais, P.P.P.; Braatz de Andrade, L.R.; Granato, I.; Crossa, J.
Publication - EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture(Genetics Society of America, 2021) Costa-Neto, G.; Galli, G.; Fanelli Carvalho, H.; Crossa, J.; Fritsche-Neto, R.
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