ArticleG3 (Bethesda, Md.)2022
learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- Deployment-oriented evaluation of stage-specific information for rice yield prediction across years, sites and site-year environments in southern China.Frontiers in plant science · 2026Article
- Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- Genomewide association and prediction of phenotypic stability in barley.The plant genome · 2025Article
- Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.Scientific reports · 2024Article
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.G3 (Bethesda, Md.) · 2023Article
- easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization.Bioinformatics advances · 2023Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial breeding data with machine learning-based models. learnMET allows the combination of genomic information with environmental data such as climate and/or soil characteristics. Notably, the package offers the possibility of incorporating weather data from field weather stations, or to retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated over specific periods of time based on naive (for instance, nonoverlapping 10-day windows) or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient-boosted decision trees, random forests, stacked ensemble models, and multilayer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with multi-environment trial experimental data in a user-friendly way. The package is published under an MIT license and accessible on GitHub.
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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.