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
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Identifier |
https://hdl.handle.net/11529/10548273
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Creator |
Crossa, Jose
Martini, Johannes Gianola, Daniel Pérez-Rodríguez, Paulino Burgueño, Juan Singh, Ravi Juliana, Philomin Montesinos-López, Osval A. Cuevas, Jaime |
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Publisher |
CIMMYT Research Data & Software Repository Network
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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.
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Subject |
Agricultural Sciences
Agricultural research Wheat Triticum aestivum Deep learning Deep kernel Genomic best linear unbiased predictor |
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Language |
English
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Date |
2019
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Contributor |
Shrestha, Rosemary
Genetic Resources Program (GRP) Biometrics and Statistics Unit (BSU) Global Wheat Program (GWP) CGIAR Research Program on Wheat (WHEAT) CGIAR |
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Type |
Experimental data, Genotypic data, Phenotypic data
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