subject area of
- Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions.
- 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-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.
- Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
- Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials.