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Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Author: Crossa, J.
Author: Martini, J.W.R.
Author: Gianola, D.
Author: Perez-Rodriguez, P.
Author: Jarquín, D.
Author: JULIANA P.
Author: Montesinos-Lopez, O.A.
Author: Cuevas, J.
Year: 2019
ISSN: 1664-8021 (Print)
URI: https://hdl.handle.net/10883/20617
Format: PDF
Language: English
Publisher: Frontiers
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Switzerland
Issue: art. 1168
Volume: 10
DOI: 10.3389/fgene.2019.01168
Description: 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 non-additive Gaussian kernel (GK), as well as to the conventional additive genomic best linear unbiased predictor (GBLUP/GB). We used two real wheat data sets for benchmarking these methods. On average, AK and GK outperformed DL and GB. The gain in terms of prediction performance of AK and GK over DL and GB was not large, but AK and GK have the advantage that only one parameter, the number of layers (AK) or the bandwidth parameter (GK), has to be tuned in each method. Furthermore, although AK and GK had similar performance, deep kernel AK is easier to implement than GK, since the parameter "number of layers" is more easily determined than the bandwidth parameter of GK. Comparing AK and DL for the data set of year 2015-2016, the difference in performance of the two methods was bigger, with AK predicting much better than DL. On this data, the optimization of the hyperparameters for DL was difficult and the finally used parameters may have been suboptimal. Our results suggest that AK is a good alternative to DL with the advantage that practically no tuning process is required.
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Agrovoc: PLANT BREEDING
Related Datasets: http://hdl.handle.net/11529/10548273
Journal: Frontiers in Genetics


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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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