TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Montesinos-Lopez,A.
AU  - Mosqueda-Gonzalez,B.A.
AU  - Bentley,A.R.
AU  - Lillemo,M.
AU  - Varshney,R.K.
AU  - Crossa,J.
TI  - A new deep learning calibration method enhances genome-based prediction of continuous crop traits
SN  - 1664-8021
PY  - 2021///
CY  - Switzerland
PB  - Frontiers
KW  - Marker-assisted selection
KW  - AGROVOC
KW  - Genomics
KW  - Machine learning
KW  - Plant breeding
N1  - Peer review; Open Access
N2  - Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding
UR  - https://hdl.handle.net/10883/21810
T2  - Frontiers in Genetics
DO  - https://doi.org/10.3389/fgene.2021.798840
ER  -