Título
Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding
Autor
Carolina Rivera-Amado
Francisco Pinto
Francisco Javier Pinera-Chavez
David González-Diéguez
Paulino Pérez-Rodríguez
Huihui Li
Osval Antonio Montesinos-Lopez
Jose Crossa
Nivel de Acceso
Acceso Abierto
Descripción
Abstracto - 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.
Editor
International Maize and Wheat Improvement Center
Fecha de publicación
2023
Tipo de recurso
Dataset
Recurso de información
Repositorio Orígen
Repositorio Institucional de Datos y Software de Investigación del CIMMYT
Descargas
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