Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and biological discovery.

Abstract excerpt

Traditional genotype-to-phenotype models depend heavily on direct mappings that achieve only modest accuracy, forcing breeders to conduct large, costly field trials to maintain or marginally improve genetic gain. Models that incorporate intermediate molecular phenotypes can achieve higher predictive fit, but remain impractical since such data are unavailable at deployment or design time. Biology-informed neural...

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• Crops, Agricultural
• Genome, Plant
• Genome-Wide Association Study
• Genomics
• Multiomics