ArticleG3 (Bethesda, Md.)2019
Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Genomic prediction accuracy for low hydrogen cyanide selection in fresh cassava roots. Comparative model analysis.BMC plant biology · 2026Article
- Multimodal deep learning improves cross-environment prediction of durum wheat yield components.BMC plant biology · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Genome-wide association study identifies consistent genomic loci for yield and nutritional quality in Ethiopian sorghum landraces.The plant genome · 2026Article
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Near-Infrared Spectroscopy-Based Phenomics Data Can Improve Genomic Prediction of Agronomic and Grain Quality Traits Across Multi-Environment Sorghum Hybrid Trials.Plants (Basel, Switzerland) · 2025Article
- Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops.Plants (Basel, Switzerland) · 2025Review
- Genomic selection in pig breeding: comparative analysis of machine learning algorithms.Genetics, selection, evolution : GSE · 2025Article
- Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Article
- Integrative multi-environmental genomic prediction in apple.Horticulture research · 2025Article
- Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants.Plant methods · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Use of continuous genotypes for genomic prediction in sugarcane.The plant genome · 2024Article
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- Genetic selection for growth, wood quality and resin traits of potential Slash pine for multiple industrial uses.Forestry research · 2024Article
Corrections and comments
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.
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