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Subject: Agricultural Sciences Publication Date: 2019 Contributor Name: Biometrics and Statistics Unit (BSU) Contributor Name: Genetic Resources Program (GRP)

1 to 5 of 5 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 Deep kernel of genomic and near infrared predictions in multi-environment breeding trials 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...
Dataset / Software Multi-trait multi-environment genomic prediction of durum wheat Aug 12, 2019 - CIMMYT Research Data Montesinos-López, Osval A ; Montesinos-López, Abelardo; Tuberosa, Roberto; Maccaferri, Marco; Sciara, Giuseppe; Ammar, Karim; Crossa, Jose, 2019, "Multi-trait multi-environment genomic prediction of durum wheat", hdl:11529/10548262, CIMMYT Research Data & Software Repository Network, V1 In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also w...
Dataset / Software Deep kernel and deep learning for genomic-based prediction Aug 15, 2019 - CIMMYT Research Data Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1 Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...
Dataset / Software Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding Dec 6, 2019 - CIMMYT Research Data Cerón-Rojas, J. Jesus; Crossa, Jose, 2019, "Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding", hdl:11529/10548356, CIMMYT Research Data & Software Repository Network, V1 Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the...

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

Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

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...

Dataset / Software

Multi-trait multi-environment genomic prediction of durum wheat

Aug 12, 2019 - CIMMYT Research Data

Montesinos-López, Osval A ; Montesinos-López, Abelardo; Tuberosa, Roberto; Maccaferri, Marco; Sciara, Giuseppe; Ammar, Karim; Crossa, Jose, 2019, "Multi-trait multi-environment genomic prediction of durum wheat", hdl:11529/10548262, CIMMYT Research Data & Software Repository Network, V1

In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also w...

Dataset / Software

Deep kernel and deep learning for genomic-based prediction

Aug 15, 2019 - CIMMYT Research Data

Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1

Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...

Dataset / Software

Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding

Dec 6, 2019 - CIMMYT Research Data

Cerón-Rojas, J. Jesus; Crossa, Jose, 2019, "Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding", hdl:11529/10548356, CIMMYT Research Data & Software Repository Network, V1

Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the...

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