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

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018)

Artículo

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES

Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat

Jose Crossa Osval Antonio Montesinos-Lopez Morten Lillemo (2024)

Artículo

Multispectral Imaging Grain Yield Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN YIELDS HIGH-THROUGHPUT PHENOTYPING SPRING WHEAT

Determining factors of durum wheat bread loaf volume and alveograph characteristics under optimal, drought and heat stress conditions

Maryke Labuschagne Carlos Guzman Jose Crossa Angeline van Biljon (2023)

Artículo

Loaf Volume Durum Wheat Flour Protein Content CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ALVEOGRAPHS HARD WHEAT HEAT STRESS DROUGHT STRESS

Response to early generation genomic selection for yield in wheat

Jose Crossa Susanne Dreisigacker Gregorio Alvarado Jesse Poland (2021)

Genotyping-by-Sequencing data of a training set consisting of 1340 CIMMYT elite lines and a validation set including 1925 F2 individual derived from 38 crosses and 21 parents from the training set.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Multi-trait Bayesian decision for parental selection

Jose Crossa Fernando Henrique Toledo Paulino Pérez-Rodríguez (2020)

The files included in this study contains the data used with three promising multivariate loss functions: Kullback-Leibler (KL); the Energy Score; and the Multivariate Asymmetric Loss (MALF); to select the best performing parents for the next breeding cycle in two extensive real wheat data sets.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Yield data for pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat

Sivakumar Sukumaran Jose Crossa DIEGO JARQUIN Matthew Paul Reynolds (2016)

This study contains spring wheat yield data (1st, 2nd, and 3rd WYCYTs and 1st, 2nd, 3rd and 4th SATYNs) from 136 international environments that were used to evaluate the predictive ability of different models in diverse environments by modeling G×E using the pedigree-derived additive relationship matrix (A matrix).

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat

Ravi Singh Kelly Robbins Jose Crossa Alison Bentley (2022)

In multi-environment yield trials, the use of sparse testing genomic selection enables increasing selection intensity or testing environments. The data presented in this dataset were used in the evaluation of different sparse testing genomic selection strategies in the early yield testing stage of CIMMYT spring wheat breeding pipeline. Phenotypic, genotypic, and coefficient of parentage data are provided. The germplasm is made up of multiple populations each with small family sizes. The findings of the study are detailed in an accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

SNP Allele Frequencies and Descriptive Data of 15,384 CIMMYT Germplasm Bank Maize Landrace Accessions

Jorge Franco Jose Crossa jiafa chen Sarah Hearne (2019)

Data and data descriptions supporting the journal article "The impact of sample selection strategies on genetic diversity and representativeness in germplasm bank collections"

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

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

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018)

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

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Statistical machine-learning methods for genomic prediction using the SKM library

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023)

Artículo

Sparse Kernel Methods R package Statistical Machine Learning Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MACHINE LEARNING GENOMICS METHODS