| Handle: | https://hdl.handle.net/11529/10548885 |
|---|---|
| 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 |
| Licencia: | Atribución |
| Matería: | AGRICULTURAL SCIENCES, CIENCIAS AGRARIAS, |
| Resumen o descripción: | 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: | None |
| Fecha de Publicación: | 2023 |
| Tipo de Publicación: | |
| Idioma: | eng |