learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data
2022 | journal article. A publication with affiliation to the University of Göttingen.
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- Authors
- Westhues, Cathy C.; Simianer, Henner ; Beissinger, Timothy M.
- Abstract
- 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.
- Issue Date
- 2022
- Journal
- G3 Genes|Genomes|Genetics
- Organization
- Fakultät für Agrarwissenschaften ; Department für Nutzpflanzenwissenschaften ; Abteilung Zuchtmethodik der Pflanze ; Zentrum für Integrierte Züchtungsforschung
- eISSN
- 2160-1836
- Language
- English
- Sponsor
- Open-Access-Publikationsfonds 2022