In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression
models have been used. This study assessed the predictive ability of linear and non-linear …

The availability of high density panels of molecular markers has prompted the adoption of
genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi …

Cross-validation of methods is an essential component of genome-enabled prediction of
complex traits. We develop formulae for computing the predictions that would be obtained …

Background Multi-layer perceptron (MLP) and radial basis function neural networks
(RBFNN) have been shown to be effective in genome-enabled prediction. Here, we …

Key message Our simulation results clarify the areas of applicability of nine prediction
methods and suggest the factors that affect their accuracy at predicting empirical traits …

Background The goal of genome-wide prediction (GWP) is to predict phenotypes based on
marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The …

Genome-wide association study (GWAS) and genomic prediction (GP) are extensively
employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 …

The usefulness of genomic prediction in crop and livestock breeding programs has
prompted efforts to develop new and improved genomic prediction algorithms, such as …

Key message Genomic predictions across environments and within populations resulted in
moderate to high accuracies but across-population genomic prediction should not be …

Deep learning (DL) is a promising method for genomic-enabled prediction. However, the
implementation of DL is difficult because many hyperparameters (number of hidden layers …