Journal article
A new deep learning calibration method enhances Genome-Based prediction of continuous crop traits
Frontiers in Genetics, Vol.12, Art. 798840
2021
Abstract
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.
Details
- Title
- A new deep learning calibration method enhances Genome-Based prediction of continuous crop traits
- Authors/Creators
- O.A. Montesinos-López (Author/Creator) - Universidad de ColimaA. Montesinos-López (Author/Creator) - Universidad de GuadalajaraB.A. Mosqueda-González (Author/Creator) - Instituto Politécnico NacionalA.R. Bentley (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoM. Lillemo (Author/Creator) - Norwegian University of Life SciencesR.K. Varshney (Author/Creator) - International Crops Research Institute for the Semi-Arid TropicsJ. Crossa (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y Trigo
- Publication Details
- Frontiers in Genetics, Vol.12, Art. 798840
- Publisher
- Frontiers
- Identifiers
- 991005540810607891
- Copyright
- © 2021 Montesinos-López et al.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; State Agricultural Biotechnology Centre
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
20 File views/ downloads
49 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.51 Dairy & Animal Sciences
- 3.51.115 Livestock Reproduction
- Web Of Science research areas
- Genetics & Heredity
- ESI research areas
- Molecular Biology & Genetics