A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis
Abstract Sepsis is a complex systemic inflammatory syndrome that currently lacks stable and specific biomarkers. Multi-omics integration combined with machine learning and single-cell analysis offers new approaches for elucidating molecular mechanisms and nominating candidate regulators for further...
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| Principais autores: | , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
Nature Portfolio
2026-01-01
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| Serier: | Scientific Reports |
| Fag: | |
| Online adgang: | https://doi.org/10.1038/s41598-026-37342-z |
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