“outperformed Bayesian models”

Claim · model-written summary

NN-Bayes outperformed linear models for GWAS and genomic prediction in simulations.

Taxon

soye1

Outcome

performance of NN-Bayese0

Direction

significantly bettere0

Qualifier

dominance and epistatic effectse0

Comparator

conventional linear modelse0

Verbatim

e0

“In simulation studies with dominance and epistatic effects, performance of NN-Bayes was significantly better than conventional linear models for both GWAS and whole-genome prediction, and the differences on prediction accuracy were substantial in magnitude.”

e1

“for the soy dataset”

Claim · model-written summary

Performer models significantly outperformed traditional approaches in barley yield prediction, with R2 of 0.820 versus 0.807 and 0.076.

Taxon

barleyHordeum vulgaree1

Outcome

R2 scoree0

Direction

outperforme0

Value

0.820e0

Qualifier

0.807 and 71.63, and 0.076 and 149.78e0

Comparator

traditional approachese0

Setting

Norwaye1

Verbatim

e0

“performer-based models significantly outperform the traditional approaches, achieving an R2 score of 0.820 and a root mean squared error of 69.05, compared to 0.807 and 71.63, and 0.076 and 149.78 for the best traditional neural network and traditional Bayesian approach respectively”

t0

“crop yield prediction”

e1

“predicting barley yields across 8 different locations in Norway for the years 2017 and 2018”