Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Pre...
Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids
About this item
Full title
Author / Creator
Publisher
Germany
Journal title
Language
English
Formats
Publication information
Publisher
Germany
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Scope and Contents
Contents
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 genomic data for improved accuracy and efficiency in genetic an...
Alternative Titles
Full title
Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_3174463007
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_3174463007
Other Identifiers
ISSN
2198-3844
E-ISSN
2198-3844
DOI
10.1002/advs.202412423
