learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.

Autor: Westhues CC; Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, 37075 Goettingen, Germany.; Center for Integrated Breeding Research, University of Goettingen, 37075 Goettingen, Germany., Simianer H; Center for Integrated Breeding Research, University of Goettingen, 37075 Goettingen, Germany.; Animal Breeding and Genetics Group, Department of Animal Sciences, University of Gottingen, 37075 Gottingen, Germany., Beissinger TM; Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, 37075 Goettingen, Germany.; Center for Integrated Breeding Research, University of Goettingen, 37075 Goettingen, Germany.
Jazyk: angličtina
Zdroj: G3 (Bethesda, Md.) [G3 (Bethesda)] 2022 Nov 04; Vol. 12 (11).
DOI: 10.1093/g3journal/jkac226
Abstrakt: 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.
(© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)
Databáze: MEDLINE
Externí odkaz: