ArticleFrontiers in genetics2019
Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Integrating genomic and phenomic data improves prediction of key traits in potato breeding.Horticulture research · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Breeding innovations focused on drought tolerance, and Nitrogen and phosphorus uptake, for sustainable and climate-resilient sugarcane.Frontiers in plant science · 2026Review
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Introducing CHiDO-A No Code Genomic Prediction software implementation for the characterization and integration of driven omics.The plant genome · 2025Article
- Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions.Frontiers in plant science · 2025Article
- Enhancing wheat genomic prediction by a hybrid kernel approach.Frontiers in plant science · 2025Article
- Use of continuous genotypes for genomic prediction in sugarcane.The plant genome · 2024Article
- Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.BMC genomic data · 2023Article
- Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.The plant genome · 2023Article
- Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato.Plants (Basel, Switzerland) · 2023Article
- Results from rapid-cycle recurrent genomic selection in spring bread wheat.G3 (Bethesda, Md.) · 2023Article
- Comparing artificial-intelligence techniques with state-of-the-art parametric prediction models for predicting soybean traits.The plant genome · 2023Article
- Multi-environment Genomic Selection in Rice Elite Breeding Lines.Rice (New York, N.Y.) · 2023Article
- Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.Frontiers in plant science · 2023Article
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data.The plant genome · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to be tuned. For this reason, deep kernel methods, which only require defining the number of layers, may be an attractive alternative. Deep kernel methods emulate DL models with a large number of neurons, but are defined by relatively easily computed covariance matrices. In this research, we compared the genome-based prediction of DL to a deep kernel (arc-cosine kernel, AK), to the commonly used non-additive Gaussian kernel (GK), as well as to the conventional additive genomic best linear unbiased predictor (GBLUP/GB). We used two real wheat data sets for benchmarking these methods. On average, AK and GK outperformed DL and GB. The gain in terms of prediction performance of AK and GK over DL and GB was not large, but AK and GK have the advantage that only one parameter, the number of layers (AK) or the bandwidth parameter (GK), has to be tuned in each method. Furthermore, although AK and GK had similar performance, deep kernel AK is easier to implement than GK, since the parameter "number of layers" is more easily determined than the bandwidth parameter of GK. Comparing AK and DL for the data set of year 2015-2016, the difference in performance of the two methods was bigger, with AK predicting much better than DL. On this data, the optimization of the hyperparameters for DL was difficult and the finally used parameters may have been suboptimal. Our results suggest that AK is a good alternative to DL with the advantage that practically no tuning process is required.
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