Title
Deep kernel and deep learning for genomic-based prediction
Author
Jose Crossa
Paulino Pérez-Rodríguez
Juan Burgueño
Ravi Singh
Philomin Juliana
Osval Antonio Montesinos-Lopez
Jaime Cuevas
Access level
Open Access
Description
Abstract - 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.
Publisher
International Maize and Wheat Improvement Center
Publish date
2019
Resource Type
Dataset
Information Resource
Source repository
Repositorio Institucional de Datos y Software de Investigación del CIMMYT
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