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Replication Data for: A multivariate Poisson deep learning model for genomic prediction of count data

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: A multivariate Poisson deep learning model for genomic prediction of count data
 
Identifier https://hdl.handle.net/11529/10548438
 
Creator Montesinos-López, Osval A.
Montesinos-López, José Cricelio
Singh, Pawan
Lozano-Ramirez, Nerida
Barrón-López, Alberto
Montesinos-López, Abelardo
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Genomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets were used to support research on efficient methodologies for multivariate count data outcomes including a multivariate Poisson deep neural network (MPDN) model, a conventional multivariate generalized Poisson regression model, and a univariate Poisson deep learning models. The results of the analyses are presented in a corresponding publication.
 
Subject Agricultural Sciences
Triticum aestivum
Genomic selection
Agricultural research
Wheat
Genomic prediction
Count data
Multivariate Poisson deep neural network
Poisson regression models
 
Language English
 
Date 2020
 
Contributor Dreher, Kate
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Maize (MAIZE)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Cornell University
Kansas State University
 
Type Phenotypic data
Genotypic data
Experimental data