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
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Identifier |
https://hdl.handle.net/11529/10548885
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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 |
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Publisher |
CIMMYT Research Data & Software Repository Network
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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. |
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Subject |
Agricultural Sciences
Plant Breeding Grain yield Thousand grain weight Canopy normalized difference vegetation index Agricultural research Triticum aestivum Wheat |
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Language |
English
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Date |
2023
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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 |
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Type |
Experimental data
Phenotypic data Genotypic data |
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