ArticleThe plant genome2021
The application of pangenomics and machine learning in genomic selection in plants.
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 32 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- Empowering Plant Biotechnology Research: Super-Pangenomes as a Novel Arsenal for Crop Breeding and Improvement.Molecular biotechnology · 2026Review
- Challenges in Bringing Pangenome Research Into Breeding: A Case Study in Rice.Plant biotechnology journal · 2026Review
- Haplotype applications in genomic selection.Genome biology · 2026Review
- Benchmarking hyperparameter optimization strategies for crop genomic prediction.Frontiers in genetics · 2026Article
- Understanding plant phenotypes in crop breeding through explainable AI.Plant biotechnology journal · 2025Review
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Mutual information stacking method for prediction of the growth traits in pigs.Briefings in bioinformatics · 2025Article
- Genomic selection in pig breeding: comparative analysis of machine learning algorithms.Genetics, selection, evolution : GSE · 2025Article
- Brassica Panache: A multi-species graph pangenome representing presence absence variation across forty-one Brassica genomes.The plant genome · 2025Article
- Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum.BMC plant biology · 2024Article
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- Machine learning for genomic and pedigree prediction in sugarcane.The plant genome · 2024Article
- The complex relationship between disease resistance and yield in crops.Plant biotechnology journal · 2024Review
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Technological Development and Advances for Constructing and Analyzing Plant Pangenomes.Genome biology and evolution · 2024Review
- Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling.Scientific reports · 2024Article
- Multiomics Research: Principles and Challenges in Integrated Analysis.Biodesign research · 2024Review
- 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
- The Progression in Developing Genomic Resources for Crop Improvement.Life (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state-of-the-art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome-based approaches in crop breeding, discuss machine learning-specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.