EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.

ABSTRACT: Phenotypic variation results from the combination of genotype, the environment, and their interaction. The ability to quantify the relative contributions of genetic and environmental factors to complex traits can help in breeding crops with superior adaptability for growth in varied environments. Here, we developed and extensively evaluated the performance of an explainable machine-learning framework named explainable genotype-by-environment interactions prediction (EXGEP) to accurately predict the grain yield in crops. To assess the performance of EXGEP, we applied it to a dataset comprising 70 693 phenotypic records of grain yield traits for 3793 hybrids (also including both genotype and environmental condition data). When used with four different combinations of genotypes and environmen

SUBMITTER: Yu T 

PROVIDER: S-EPMC12354955 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature