Genomic selection is revolutionizing plant breeding and therefore methods that improve
prediction accuracy are useful. For this reason, active research is being conducted to build …

Multi-trait and multi-environment data are common in animal and plant breeding programs.
However, what is lacking are more powerful statistical models that can exploit the correlation …

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction
methods have been proposed including the standard additive genetic effect model for which …

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 …

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement
in plants. Traditional methods typically use linear regression models with clear assumptions; …

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from
complex data such as image, text, or video. However, its ability to predict phenotypic values …

Main conclusion Deep learning is a promising technology to accurately select individuals
with high phenotypic values based on genotypic data. Abstract Genomic selection (GS) is a …

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop
representing just 5–7% of the world's total wheat crop, it is a staple food in Mediterranean …

Genomic selection (GS) is transforming the field of plant breeding and implementing models
that improve prediction accuracy for complex traits is needed. Analytical methods for …

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