CIMMYT Research Data & Software Repository Network

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Contributor Name: CGIAR Research Program on Wheat (WHEAT) Contributor Name: Genetic Resources Program (GRP) Author Name: Crossa, Jose Author Name: Gianola, Daniel Keyword Term: Wheat Keyword Term: Days to heading

1 to 3 of 3 Results

Dataset / Software Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments Jul 12, 2019 - CIMMYT Research Data Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3 In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...
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 Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits Sep 28, 2018 - CIMMYT Research Data Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", hdl:11529/10548134, CIMMYT Research Data & Software Repository Network, V1 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.

Dataset / Software

Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments

Jul 12, 2019 - CIMMYT Research Data

Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3

In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...

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

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

Sep 28, 2018 - CIMMYT Research Data

Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", hdl:11529/10548134, CIMMYT Research Data & Software Repository Network, V1

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.

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