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A review of deep learning applications for genomic selection

2021; BioMed Central; Volume: 22; Issue: 1 Linguagem: Inglês

10.1186/s12864-020-07319-x

1471-2164

Osval A. Montesinos‐López, Abelardo Montesinos‐López, Paulino Pérez‐Rodríguez, José Alberto Barrón‐López, Johannes W. R. Martini, Silvia B. Fajardo-Flores, Laura S. Gaytán-Lugo, Pedro C. Santana-Mancilla, José Crossa,

Genetics and Plant Breeding

Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns.