A joint learning approach for genomic prediction in polyploid grasses (2022)
Aono, Alexandre Hild; Ferreira, Rebecca Caroline Ulbricht; Moraes, Aline da Costa Lima; Lara, Letícia Aparecida de Castro 
; Pimenta, Ricardo José Gonzaga; Costa, Estela Araujo; Pinto, Luciana Rossini; Landell, Marcos Guimarães de Andrade; Santos, Mateus Figueiredo; Jank, Liana; Barrios, Sanzio Carvalho Lima; Valle, Cacilda Borges do; Chiari, Lucimara; Garcia, Antonio Augusto Franco ; Kuroshu, Reginaldo Massanobu; Lorena, Ana Carolina; Gorjanc, Gregor; Souza, Anete Pereira de
Fonte: Scientific Reports. Unidade: ESALQ
Assuntos: APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, CAPIM BRAQUIÁRIA, CAPIM MOMBAÇA, CROMOSSOMOS VEGETAIS, GENÔMICA, MELHORAMENTO GENÉTICO VEGETAL, SELEÇÃO GENÉTICA
ABNT
AONO, Alexandre Hild et al. A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, v. 12, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.1038/s41598-022-16417-7. Acesso em: 11 nov. 2024.APA
Aono, A. H., Ferreira, R. C. U., Moraes, A. da C. L., Lara, L. A. de C., Pimenta, R. J. G., Costa, E. A., et al. (2022). A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, 12, 1-17. doi:10.1038/s41598-022-16417-7NLM
Aono AH, Ferreira RCU, Moraes A da CL, Lara LA de C, Pimenta RJG, Costa EA, Pinto LR, Landell MG de A, Santos MF, Jank L, Barrios SCL, Valle CB do, Chiari L, Garcia AAF, Kuroshu RM, Lorena AC, Gorjanc G, Souza AP de. A joint learning approach for genomic prediction in polyploid grasses [Internet]. Scientific Reports. 2022 ; 12 1-17.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1038/s41598-022-16417-7Vancouver
Aono AH, Ferreira RCU, Moraes A da CL, Lara LA de C, Pimenta RJG, Costa EA, Pinto LR, Landell MG de A, Santos MF, Jank L, Barrios SCL, Valle CB do, Chiari L, Garcia AAF, Kuroshu RM, Lorena AC, Gorjanc G, Souza AP de. A joint learning approach for genomic prediction in polyploid grasses [Internet]. Scientific Reports. 2022 ; 12 1-17.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1038/s41598-022-16417-7
