Data augmentation enhances plant-genomic-enabled predictions
Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with da...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/159826 |
Similar Items: Data augmentation enhances plant-genomic-enabled predictions
- Feature engineering of environmental covariates improves plant genomic-enabled prediction
- A graph model for genomic prediction in the context of a linear mixed model framework
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
- Boosting genomic prediction transferability with sparse testing
- Optimizing genomic prediction with transfer learning under a ridge regression framework
- Balancing sensitivity and specificity enhances top and bottom ranking in genomic prediction of cultivars