Título

Deep kernel and deep learning for genomic-based prediction

Autor

Jose Crossa

Paulino Pérez-Rodríguez

Juan Burgueño

Ravi Singh

Philomin Juliana

Osval Antonio Montesinos-Lopez

Jaime Cuevas

Nivel de Acceso

Acceso Abierto

Descripción

Abstracto - 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.

Editor

International Maize and Wheat Improvement Center

Fecha de publicación

2019

Tipo de recurso

Dataset

Recurso de información

Repositorio Orígen

Repositorio Institucional de Datos y Software de Investigación del CIMMYT

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