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

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

New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes

Osval Antonio Montesinos-Lopez Francisco Javier Martin Vallejo Jose Crossa Philomin Juliana Ravi Singh (2018)

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

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA