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Maize (Zea mays L.) has been a focus of scientific research and breeding for over a century. It is also one of the most economically important crops in the world, with a value of approximately US$50 billion per year in the United States alone. Additionally, maize has long been the model species of choice for the study and exploitation of hybrid vigor, and it continues to be one of the world's most efficient converters of photosynthetic energy into starch. This review discusses the history and future of maize predictive breeding in the context of both genotype centric methods, and those focusing on genotype × environment × management interactions. Current prediction challenges are highlighted, as well as important advances in technology, methods, datasets, interdisciplinary collaborations, and scientific culture that will enable accelerated progress in predictive maize (and other crop species) breeding for years to come. 

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: ( i ) gene-family–guided splitting and ( ii ) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3′ UTR is more important for fine-tuning mRNA abundance levels while the 5′ UTR is more important for large-scale changes.