Claim · model-written summary
Forward prediction accuracy ranged from 0.29 to 0.55.
- Taxon
- Winter Wheat
Triticum aestivumt0 - Outcome
- prediction accuracies for different traitse0
- Value
- 0.29-0.55e0
- Qualifier
- forwarde0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions”
t0“Winter Wheat”
Title
Karansher Sandhu; Meriem Aoun; Craig Morris; et al. Biology 2021. doi:10.3390/biology10070689
Other claims from this paper (6)
- Method / resource Cross-validation prediction accuracy across quality traits ranged from 0.45 to 0.81.
- Method / resource Across-location prediction accuracy ranged from 0.27 to 0.50.
- Method / resource Forward accuracy increased over time with more training data, especially for machine and deep learning.
- Method / resource Deep learning outperformed RRBLUP and Bayesian models in all prediction scenarios.
- Recommendation The authors supported prediction of quality traits in early generations to advance superior genotypes.
- Recommendation The authors supported using machine and deep learning in large breeding programs to predict complex traits.