Ensembles of Graph Attention Networks Supervised by Genotype-to-Phenotype Structures Improved Genomic Prediction Performance
This study demonstrates that while incorporating data-driven genotype-to-phenotype structures into individual Graph Attention Networks did not consistently improve genomic prediction for maize flowering time, ensembling models across a continuum of such structures significantly enhanced prediction performance by integrating complementary information from diverse biological representations.