ArticleGenes2018
Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in forensic science: a systematic review. Part II: long-range postmortem interval estimation.International journal of legal medicine · 2026Pooled it
- Human Decomposition and Time Since Death: Persistent Challenges and Future Directions of Postmortem Interval Estimation in Forensic Anthropology.American journal of biological anthropology · 2024Pooled it
- Characterisation of microbial succession for the estimation of PMI and PMSI in terrestrial and aquatic environments.Forensic science, medicine, and pathology · 2026Article
- Application of artificial intelligence in the determination of the postmortem interval: Systematic review of the literature and metaanalysis.Forensic science, medicine, and pathology · 2026Review
- Insects shape the cadaver decomposition microbiome and postmortem interval estimation accuracy.mSystems · 2026Article
- Endogenous and Exogenous Small RNA Signatures as Novel Tools for Postmortem Interval Determination.Biomolecules · 2026Article
- Forensic applications of the skin microbiome: a structured narrative review of donor association, contact inference, and crime-scene reconstruction.Frontiers in microbiology · 2026Review
- Estimating Postmortem Interval of Buried Pig Carcasses by Integrating Microbial Succession Patterns with Machine Learning Algorithms.Microorganisms · 2025Article
- Leguminous and gramineous plant silages display unique characteristics of bacterial community ecology.Environmental microbiome · 2025Article
- The microbiome and volatile organic compounds reflecting the state of decomposition in an indoor environment.Science & justice : journal of the Forensic Science Society · 2025Article
- Comprehensive Analysis of Vaginal and Gut Microbiome Alterations in Endometriosis Patients.International journal of women's health · 2025Article
- Advancing time-since-interval estimation for clandestine graves: a forensic ecogenomics perspective into burial and translocation timelines using massively parallel sequencing.Frontiers in microbiology · 2025Review
- New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach.Frontiers in microbiology · 2025Article
- Wildlife parasitology: sample collection and processing, diagnostic constraints, and methodological challenges in terrestrial carnivores.Parasites & vectors · 2024Review
- Evaluation of the Microbiome Identification of Forensically Relevant Biological Fluids: A Pilot Study.Diagnostics (Basel, Switzerland) · 2024Article
- Seasonal mouse cadaver microbial study: rupture time and postmortem interval estimation model construction.PeerJ · 2024Article
- Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human decomposition soils.PloS one · 2024Article
- Review
- Integration of Phenotypes in Microbiome Networks for Designing Synthetic Communities: a Study of Mycobiomes in the Grafted Tomato System.Applied and environmental microbiology · 2023Article
- Dissecting the microbial community structure of internal organs during the early postmortem period in a murine corpse model.BMC microbiology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Death investigations often include an effort to establish the postmortem interval (PMI) in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head), gene markers (16S ribosomal RNA (rRNA), 18S rRNA, internal transcribed spacer regions (ITS)), and taxonomic levels (sequence variants, species, genus, etc.). We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.