Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials

Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials

Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to be tuned. For this reason, deep kernel methods,...

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Bibliographic Details
Main Authors: José Crossa, Johannes W.R. Martini, Daniel Gianola, Paulino Pérez-Rodríguez, Diego Jarquin, Philomin Juliana, Osval Montesinos-López, Jaime Cuevas
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-12-01
Series:Frontiers in Genetics
Subjects:
deep learning
deep kernel
genomic selection
kernel methods
artificial neural networks
genomic × environment interaction
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2019.01168/full

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https://www.frontiersin.org/article/10.3389/fgene.2019.01168/full

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