ArticleThe plant genome2018
Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.
Article in The plant genome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genetic insights into bacterial wilt resistance using genomic prediction and association mapping in blueberry.The plant genome · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Enhancing genomic prediction for key production traits in chickens through ultrasound phenotyping and multi-model comparative analysis.Journal of animal science and biotechnology · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- A comprehensive assessment of climate and topographic changes to identify conservation gaps for the critically endangered speciesFrontiers in plant science · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Resistance gene againstFrontiers in plant science · 2026Review
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 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
- A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips.Journal of animal science and biotechnology · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
- Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat.Scientific reports · 2025Article
- Article
- optRF: Optimising random forest stability by determining the optimal number of trees.BMC bioinformatics · 2025Article
- Genomic selection in pig breeding: comparative analysis of machine learning algorithms.Genetics, selection, evolution : GSE · 2025Article
- Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).The plant genome · 2025Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1-5, 1-9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state-of-the-art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.
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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.