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 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 …

Kernel methods are flexible and easy to interpret and have been successfully used in
genomic-enabled prediction of various plant species. Kernel methods used in genomic …

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not
rare in animal and plant breeding programs. However, there is a lack of statistical models …

Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials
(MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the …

Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic
datasets. However, the performance of a single machine learning method in genomic …

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 …

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 …

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 …