Method / resourcemodel-written summary

Deep learning models outperformed Bayesian models across single and multiple trait and environment schemes.

A claim from Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach · 2023

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Deep learning models outperformed Bayesian models across single and multiple trait and environment schemes.

Taxon
maizeZea mayst0
Outcome
outperformed Bayesian modelse0
Direction
outperformede0
Comparator
Bayesian modelse0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Zea mays
Zea mays · TaxonAkwugo · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches”

✓ located verbatim in the source · abstract, characters 1271–1394
t0

“flowering traits in maize”

✓ located verbatim in the source · title, characters 57–82

Title

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach

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)

Record rev-4d9f34feb4a0695bfbc2165c8df3bbbfb60537df9c197194b0c1cf71531e9e45 · schema claims-v0.9 · position 3 in the model’s answer