DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants
Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/132711 |
Ejemplares similares: DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants
- Fast-forwarding plant breeding with deep learning-based genomic prediction
- Genomic prediction powered by multi-omics data
- Optimizing genomic prediction with transfer learning under a ridge regression framework
- Deep learning methods improve genomic prediction of wheat breeding
- Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids
- Sunpheno : a deep neural network for phenological classification of sunflower images
