ArticleFrontiers in plant science2020
Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed.
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- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.The plant genome · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Next-Generation Genotyping: Innovations Driving Plant Genomic Improvement.Life (Basel, Switzerland) · 2026Review
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- Advances and prospects of genomic-assisted breeding in roots, tubers, and banana crops.Frontiers in plant science · 2026Review
- Rootstock-mediated salinity resilience in cucumber (Cucumis sativus L.): integrating physiological traits, genomic stability and machine learning.BMC plant biology · 2025Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data.Biomolecules · 2025Article
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.Molecular breeding : new strategies in plant improvement · 2025Article
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- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
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- Evaluating genomic selection and speed breeding for Fusarium head blight resistance in wheat using stochastic simulations.Molecular breeding : new strategies in plant improvement · 2025Article
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014-2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder's toolkit for use in large scale breeding programs.
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