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Deep kernel and deep learning for genomic-based prediction

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Deep kernel and deep learning for genomic-based prediction
 
Identifier https://hdl.handle.net/11529/10548273
 
Creator Crossa, Jose
Martini, Johannes
Gianola, Daniel
Pérez-Rodríguez, Paulino
Burgueño, Juan
Singh, Ravi
Juliana, Philomin
Montesinos-López, Osval A.
Cuevas, Jaime
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel, AK) method, Gaussian kernel (GK) method and the conventional kernel method (Genomic Best Linear Unbiased Predictor, GBLUP, GB). We used two real wheat data sets for the benchmarking of these methods. We found that the GK and deep kernel AK methods outperformed the DL and the conventional GB methods, although the gain in terms of prediction performance of AK and GK was not very large but they have the advantage that no tuning parameters are required. Furthermore, although AK and GK had similar genomic-based performance, deep kernel AK is easier to implement than the GK. For this reason, our results suggest that AK is an alternative to DL models with the advantage that no tuning process is required.
 
Subject Agricultural Sciences
Agricultural research
Wheat
Triticum aestivum
Deep learning
Deep kernel
Genomic best linear unbiased predictor
 
Language English
 
Date 2019
 
Contributor Shrestha, Rosemary
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
 
Type Experimental data, Genotypic data, Phenotypic data