Enhancing late postmortem interval prediction: a pilot study integrating proteomics and machine learning to distinguish human bone remains over 15 years
Despite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.
Authors
- María de los Ángeles García (ORCID: https://orcid.org/0000-0001-8482-0361)
- G. Cabrera-Vives (ORCID: https://orcid.org/0000-0002-2720-7218)
- William Aguilar (ORCID: https://orcid.org/0000-0001-9321-0661)
- Miguel Soto
- Elena Uribe
- Juan Román (ORCID: https://orcid.org/0000-0002-9505-0953)
- Marcela Torrejón (ORCID: https://orcid.org/0000-0003-3393-9690)
- Estefanía Tarifeño-Saldivia (ORCID: https://orcid.org/0000-0001-5311-2661)
- Pablo Saldivia (ORCID: https://orcid.org/0000-0003-4326-3233)
- José L. Gutiérrez (ORCID: https://orcid.org/0000-0002-0957-2297)
- Mauricio Hernández (ORCID: https://orcid.org/0000-0002-2277-0132)
- Camila Garcés-Parra
Institutions
- University of Concepción (CL)
- University of Chile (CL)
Publication Details
- Journal
- Biological Research
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1186/s40659-024-00552-8
- Citations
- 9
- Primary Topic
- Forensic Anthropology and Bioarchaeology Studies
- Type
- article
- Field-Weighted Citation Impact
- 26.32
Funders
- Agenția Națională pentru Cercetare și Dezvoltare
- Universidad de Chile
- Universidad de Concepción
- Agencia Nacional de Investigación y Desarrollo
- CHIST-ERA