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

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

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.

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Bibliographic Details
Main Authors: Montesinos-López, Osval A., Montesinos-López, José Cricelio, Singh, Pawan, Lozano-Ramirez, Nerida, Barrón-López, Alberto, Montesinos-López, Abelardo, Crossa, Jose
Other Authors: Dreher, Kate
Format: Phenotypic data biblioteca
Language:English
Published: CIMMYT Research Data & Software Repository Network 2020
Subjects:Agricultural Sciences, Triticum aestivum, Genomic selection, Agricultural research, Wheat, Genomic prediction, Count data, Multivariate Poisson deep neural network, Poisson regression models,
Online Access:https://hdl.handle.net/11529/10548438
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https://hdl.handle.net/11529/10548438

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