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1 to 7 of 7 Results
Dataset / Software
Jan 17, 2018 - CIMMYT Research Data
Ammar, Karim; Guzman, Carlos; Dreisigacker, Susanne; Huerta, Julio; Bekele, Abeyo; Badebo, Ayele; Yahyaoui, Amor, 2018, "Phenotypic and genotypic data from the CIMMYT Durum Wheat Breeding Program", hdl:11529/10944, CIMMYT Research Data & Software Repository Network, V1
Phenotypic data collected in on-station field trials and genotypic data for breeding materials from the CIMMYT Durum Wheat breeding program are included in this study.
Dataset / Software
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
Oct 8, 2018 - CIMMYT Research Data
Cerón-Rojas, J.Jesús; Toledo, Fernando; Crossa, Jose, 2018, "Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices", hdl:11529/10548136, CIMMYT Research Data & Software Repository Network, V1
This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
Dataset / Software
Mar 26, 2019 - CIMMYT Research Data
Guzman, Carlos, 2019, "PPO activity in bread wheat breeding lines from C53IBWSN", hdl:11529/10548173, CIMMYT Research Data & Software Repository Network, V1
Breeding lines cultivated in CENEB (C. Obregon) during cropping cycle 17-18 were analyzed for PPO activity
Dataset / Software
May 16, 2019 - CIMMYT Research Data
Singh, Ravi; Mondal, Suchismita; Crespo, Leonardo; Kummar, Uttam; Imtiaz, Muhammad; Lan, Caixia; Randhawa, Mandeep; Bhavani, Sridhar; Singh, Pawan K.; Huerta, Julio; He, Xinyao; Rahman, Mokhles; Pinto, Francisco; Perez Gonzalez, Lorena; Juliana, Philomin; Singh, Daljit; Lucas, Mark; Poland, Jesse, 2016, "Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program", hdl:11529/10696, CIMMYT Research Data & Software Repository Network, V6
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
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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