Title

Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding

Author

Carolina Rivera-Amado

Francisco Pinto

Francisco Javier Pinera-Chavez

David González-Diéguez

Paulino Pérez-Rodríguez

Huihui Li

Osval Antonio Montesinos-Lopez

Jose Crossa

Access level

Open Access

Description

Abstract - In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.

Publisher

International Maize and Wheat Improvement Center

Publish date

2023

Resource Type

Dataset

Source repository

Repositorio Institucional de Datos y Software de Investigación del CIMMYT

Downloads

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