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| AONO, A. H.; FERREIRA, R. C. U.; MORAES, A. da C. L.; LARA, L. A. de C.; PIMENTA, R. J. G.; COSTA, E. A.; PINTO, L. R.; LANDELL, M. G. de A.; SANTOS, M. F.; JANK, L.; BARRIOS, S. C. L.; VALLE, C. B.; CHIARI, L.; GARCIA, A. A. F.; KUROSHU, R. M.; LORENA, A. C.; GORJANC, G.; SOUZA, A. P. de. |
| Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of... |
| Tipo: Artigo de periódico |
Palavras-chave: Cana de Açúcar; Gramínea Forrageira; Recurso Genético; Forage grasses; Genetic resources; Plant breeding; Poaceae; Polyploidy; Saccharum; Sugarcane. |
| Ano: 2022 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1150365 |
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