Claim · model-written summary
Deep learning models outperformed Bayesian models across single and multiple trait and environment schemes.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim
e0“deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches”
t0“flowering traits in maize”
Title
Freddy Mora-Poblete; Carlos Maldonado; Luma Henrique; et al. Frontiers in Plant Science 2023. doi:10.3389/fpls.2023.1153040
Other claims from this paper (7)
- Method / resource Multi-trait models increased prediction accuracy 14.4% over a single-trait single-environment approach.
- Method / resource A single trait in multiple environments improved accuracy 6.4% compared with multi-trait analysis.
- Observation GWAS identified 26 candidate genes related to flowering time.
- Observation GWAS identified 31 marker-trait associations.
- Observation The identified associations accounted for 37% of ASI variation.
- Observation The identified associations accounted for 37% of FF variation.
- Observation The identified associations accounted for 22% of MF variation.