MixINN combines mixed models and deep learning to predict genotype-environment interactions in corn trials, yielding 5.8-7.2% higher average yields when selecting top-performing genotypes compared to standard methods.
Empirical best linear un- biased prediction in cultivar trials using factor-analytic variance-covariance structures.Theoretical and Applied Genetics, 97(1):195–201
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MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction
MixINN combines mixed models and deep learning to predict genotype-environment interactions in corn trials, yielding 5.8-7.2% higher average yields when selecting top-performing genotypes compared to standard methods.