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Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
 
Identifier https://hdl.handle.net/11529/10548134
 
Creator Montesinos-López, Osval A.
Montesinos-López, Abelardo
Crossa, Jose
Gianola, Daniel
Hernández-Suarez, Carlos Moisés
Martín-Vallejo, Javier
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.
 
Subject Agricultural Sciences
Plant height
Anthesis-silking interval
Maize
Agricultural research
Wheat
Triticum aestivum
Days to heading
Days to maturity
Plant height
Prediction accuracy
 
Language English
 
Date 2018
 
Contributor Dreher, Kate
CGIAR Research Program on Wheat (WHEAT)
CGIAR Research Program on Maize (MAIZE)
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR
Global Wheat Program (GWP)
Global Maize Program (GMP)
Genetic Resources Program (GRP)
 
Type Experimental data