ArticleThe plant genome2021
Deep-learning power and perspectives for genomic selection.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- AI Agents and Agentic AI in plant breeding: new frontiers, opportunities and challenges.Frontiers in genetics · 2026Article
- Optimizing genomic prediction with transfer learning under a ridge regression framework.The plant genome · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- TPS5 and TOR signaling components are determinants ofFrontiers in plant science · 2025Article
- Genomic selection for growth characteristics in Korean red pine (Frontiers in plant science · 2024Article
- Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding.The plant genome · 2022Article
- Plant Genotype to Phenotype Prediction Using Machine Learning.Frontiers in genetics · 2022Review
- Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species.Frontiers in plant science · 2022Review
- A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction.Frontiers in genetics · 2022Article
- The Plant Genome special issue: Advances in genomic selection and application of machine learning in genomic prediction for crop improvement.The plant genome · 2021Article
- Deep-learning power and perspectives for genomic selection.The plant genome · 2021Article
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5 authors.
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Abstract
Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial machines have surpassed the ability of humans in this specific task. This is just one example of how the application of these models has surpassed human abilities and the performance of other machine-learning algorithms. For this reason, DL models have been adopted for genomic selection (GS). In this article we provide insight about the power of DL in solving complex prediction tasks and how combining GS and DL models can accelerate the revolution provoked by GS methodology in plant breeding. Furthermore, we will mention some trends of DL methods, emphasizing some areas of opportunity to really exploit the DL methodology in GS; however, we are aware that considerable research is required to be able not only to use the existing DL in conjunction with GS, but to adapt and develop DL methods that take the peculiarities of breeding inputs and GS into consideration.
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