Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice
To enrich micronutrients in rice through breeding and to identify biofortified donor lines, screening a large diversity panel of major subspecies of rice accessions for their ionomes is necessary.Inductively coupled plasma optical emission spectroscopy was deployed to profile grain ionomes from 1100...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/132704 |
Ejemplares similares: Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice
- Unraveling the genetics underlying micronutrient signatures of diversity panel present in brown rice through genome‐ionome linkages
- Whole genome sequencing-based association study to unravel genetic architecture of cooked grain width and length traits in rice
- Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging
- Ionomics: PTFI Platform
- Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR
- Metabolomics based inferences to unravel phenolic compound diversity in cereals and its implications for human gut health