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Full-Text Articles in Life Sciences
Deep Kernel And Deep Learning For Genome-Based Prediction Of Single Traits In Multienvironment Breeding Trials, José Crossa, Johannes W.R. Martini, Daniel Gianola, Paulino Pérez-Rodríguez, Diego Jarquin, Philomin Juliana, Osval Antonio Montesinos López, Jaime Cuevas
Deep Kernel And Deep Learning For Genome-Based Prediction Of Single Traits In Multienvironment Breeding Trials, José Crossa, Johannes W.R. Martini, Daniel Gianola, Paulino Pérez-Rodríguez, Diego Jarquin, Philomin Juliana, Osval Antonio Montesinos López, Jaime Cuevas
Department of Agronomy and Horticulture: Faculty Publications
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, which only require defining the number of layers, may be an attractive alternative. Deep kernel methods emulate DL models with a large number of neurons, but are defined by relatively easily computed covariance matrices. In this research, we compared the genome-based prediction of DL to a deep kernel (arc-cosine kernel, AK), to the commonly used …