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51 to 54 of 54 Results
Dataset / Software
May 16, 2019 - CIMMYT Research Data
Global Wheat Program; IWIN Collaborators; Singh, Ravi; Payne, Thomas, 2017, "23rd Semi-arid Wheat Yield Trial", hdl:11529/10987, CIMMYT Research Data & Software Repository Network, V2
CIMMYT annually distributes improved germplasm developed by its researchers and partners in international nurseries trials and experiments. The Semi-Arid Wheat Yield Trial (SAWYT) contains spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall, drought prone env...
Dataset / Software
Jun 7, 2019 - CIMMYT Research Data
Liu, Caiyun; Sukumaran, Sivakumar; Reynolds, Matthew, 2019, "Genetic data and the linkage map of Seri/Babax population", hdl:11529/10548196, CIMMYT Research Data & Software Repository Network, V1
These datasets include an updated linkage map for the Seri/Babax RIL population and the raw genotype data we used to construct the map. (1) We updated the genetic map of Seri/Babax population with 1748 non-redundant markers (1165 90K SNPs, 207 DArTseq SNPs, 183 AFLP, 111 DArT arr...
Dataset / Software
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
Jul 18, 2019 - CIMMYT Research Data
Cuevas, Jaime; Montesinos-López, Osval A; Juliana, Philomin; Pérez-Rodríguez, Paulino; Burgueño, Juan; Guzman, Carlos; Montesinos-López, Abelardo; Crossa, Jose, 2019, "Deep kernel of genomic and near infrared predictions in multi-environment breeding trials", hdl:11529/10548180, CIMMYT Research Data & Software Repository Network, V4
In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel met...
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