subject area of
- Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
- Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials.
- Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials.
- Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models
- Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.
- Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
- New approaches for delineating n‐dimensional hypervolumes
- Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials.
- Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction