Claim · model-written summary
The optimal prediction model for Arabidopsis thaliana traits depends on training data availability and trait genetic architecture.
- Taxon
- Arabidopsis thaliana
Arabidopsis thalianae3 - Outcome
- optimal modele1
- Qualifier
- dependent on availability of training data and genetic architecture of the traite1
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“with the optimal model dependent on availability of training data and genetic architecture of the trait in question”
e3“We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana”
Title
Ciaran Michael Kelly; Russell Lewis McLaughlin PLoS ONE 2024. doi:10.1371/journal.pone.0308962
Other claims from this paper (3)
- Method / resource Some machine learning methods statistically outperformed linear prediction models for genomic prediction in Arabidopsis thaliana.
- Method / resource The neural network class of predictors was the most accurate and robust for Arabidopsis thaliana traits with high heritability.
- Method / resource Genomic prediction accuracies for Arabidopsis thaliana were high under standardized laboratory conditions.