Leveraging Automated Machine Learning for Environmental Data‐Driven Genetic Analysis and Genomic Prediction in Maize Hybrids

Leveraging Automated Machine Learning for Environmental Data‐Driven Genetic Analysis and Genomic Prediction in Maize Hybrids

Abstract Genotype, environment, and genotype‐by‐environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large‐scale, multi‐environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and geno...

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Bibliographic Details
Main Authors: Kunhui He, Tingxi Yu, Shang Gao, Shoukun Chen, Liang Li, Xuecai Zhang, Changling Huang, Yunbi Xu, Jiankang Wang, Boddupalli M. Prasanna, Sarah Hearne, Xinhai Li, Huihui Li
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Advanced Science
Subjects:
environmental data
genetic analysis
genomic selection
genotype‐by‐environment interactions
machine learning
Online Access:https://doi.org/10.1002/advs.202412423
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