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Contributor Name: CGIAR Research Program on Wheat (WHEAT) Author Name: Singh, Ravi Publication Date: 2018 Contributor Name: CGIAR Contributor Name: Biometrics and Statistics Unit (BSU) Author Name: Crossa, Jose

1 to 2 of 2 Results

Dataset / Software New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes Oct 22, 2018 - CIMMYT Research Data Montesinos-López, Osval A ; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", hdl:11529/10548140, CIMMYT Research Data & Software Repository Network, V1 The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.
Dataset / Software A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Oct 26, 2018 - CIMMYT Research Data Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1 A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

Dataset / Software

New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Oct 22, 2018 - CIMMYT Research Data

Montesinos-López, Osval A ; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", hdl:11529/10548140, CIMMYT Research Data & Software Repository Network, V1

The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.

Dataset / Software

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Oct 26, 2018 - CIMMYT Research Data

Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

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