ArticleFrontiers in plant science2024
Deep learning methods improve genomic prediction of wheat breeding.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Harnessing genomics approaches for heat stress resilience in wheat: from discovery to deployment.Journal of experimental botany · 2026Review
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Multimodal deep learning improves cross-environment prediction of durum wheat yield components.BMC plant biology · 2026Article
- Nonlinear genomic selection index accelerates multi-trait crop improvement.Nature communications · 2026Article
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models.The plant genome · 2025Article
- Genomic prediction models for morpho-phenological traits in durum wheat based on Vrn, Ppd, and Rht alleles.The plant genome · 2025Article
- Fast-forwarding plant breeding with deep learning-based genomic prediction.Journal of integrative plant biology · 2025Review
- Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.Plant biotechnology journal · 2025Article
- GWAS and GS analysis revealed the selection and prediction efficiency for yield, plant morphological, and fiber quality in Gossypium barbadense.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Improving wheat grain yield genomic prediction accuracy using historical data.G3 (Bethesda, Md.) · 2025Article
- Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets.Frontiers in genetics · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.Genetics · 2024Article
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15 authors.
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Abstract
In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.
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