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Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

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Last updated 12 Juni 2024
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Frontiers Automated Machine Learning: A Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Frontiers Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
PDF) Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Weighted Kernels Improve Multi-Environment Genomic Prediction
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Frontiers Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
The contribution of dominance to phenotype prediction in a pine breeding and simulated population
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Weighted kernels improve multi-environment genomic prediction
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Frontiers Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine, BMC Genomics
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