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Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding
 
Identifier https://hdl.handle.net/11529/10548885
 
Creator Montesinos-López, Abelardo
Rivera Amado, Alma Carolina
Pinto, Francisco
Piñera Chavez, Francisco Javier
Gonzalez, David
Reynolds, Matthew
Pérez-Rodríguez, Paulino
Li, Huihui
Montesinos-López, Osval A.
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging.
The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.
 
Subject Agricultural Sciences
Plant Breeding
Grain yield
Thousand grain weight
Canopy normalized difference vegetation index
Agricultural research
Triticum aestivum
Wheat
 
Language English
 
Date 2023
 
Contributor Dreher, Kate
CGIAR Research Program on Wheat (WHEAT)
Genetic Resources Program (GRP)
Global Wheat Program (GWP)
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
Biometrics and Statistics Unit (BSU)
CGIAR
Agricultural Agreement Research Fund (JA)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Heat and Drought Wheat Improvement Consortium (HeDWIC)
International Wheat Yield Partnership (IWYP)
Foundation for Research Levy on Agricultural Products (FFL)
Foreign, Commonwealth and Development Office (FCDO)
Research Council of Norway
Foundation for Food and Agriculture Research
 
Type Experimental data
Phenotypic data
Genotypic data