The human metabolome and machine learning improves predictions of the post-mortem interval.
Saved in:
| Title: | The human metabolome and machine learning improves predictions of the post-mortem interval. |
|---|---|
| Authors: | Magnusson R; Department of Biomedical Engineering, Linköping University, Linköping, Sweden. rasmus.magnusson@liu.se., Söderberg C; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden., Ward LJ; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden., Arpe J; Department of Biomedical Engineering, Linköping University, Linköping, Sweden., Kugelberg FC; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden., Elmsjö A; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden., Green H; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.; Department of Biomedical and Clinical Sciences, Science for Life Laboratory, Linköping University, Linköping, Sweden., Nyman E; Department of Biomedical Engineering, Linköping University, Linköping, Sweden. |
| Source: | Nature communications [Nat Commun] 2026 Feb 11; Vol. 17 (1), pp. 1504. Date of Electronic Publication: 2026 Feb 11. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 2041-1723 |
|---|---|
| DOI: | 10.1038/s41467-026-69158-w |
