ReviewFrontiers in microbiology2023
Trends in forensic microbiology: From classical methods to deep learning.
Review in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From microbial data to forensic insights: systematic review of machine learning models for PMI estimation.Forensic science, medicine, and pathology · 2025Pooled it
- A dual-marker approach for forensic age prediction: integrating the salivary microbiome and antibiotic resistome.International journal of legal medicine · 2026Article
- From sepsis-or-artifact to court-ready microbial attribution: a global validation passport for forensic microbiology.Forensic science, medicine, and pathology · 2026Article
- Forging new insights in forensic microbiome studies: A 2000 to 2024 bibliometric analysis redefining the landscape.Medicine · 2026Review
- Article
- Postmortem Microbiology in Forensic Diagnostics: Interpretation of Infectious Causes of Death and Emerging Applications.Diagnostics (Basel, Switzerland) · 2026Review
- Quantifying human-environment interactions through Bayesian modeling of species-resolved microbial transfer signatures: an exploratory proof-of-concept study.Frontiers in microbiology · 2026Article
- Hide and seek with falsified medicines: Current challenges and physico-chemical and biological approaches for tracing the origin of trafficked products.Forensic science international · 2025Review
- Microbial community profiling for forensic drowning diagnosis across locations and submersion times.BMC microbiology · 2025Article
- Can Artificial Intelligence Enhance the Antimicrobial Effectiveness of Photodynamic Therapy? What We Know and What We Do Not Know?Journal of lasers in medical sciences · 2025Review
- Postmortem submersion interval prediction model based on the rat muscle microbiome.Frontiers in microbiology · 2025Article
- Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province.Frontiers in microbiology · 2025Article
- Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location.Frontiers in microbiology · 2024Review
- Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification.Frontiers in microbiology · 2024Review
- Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation.Microorganisms · 2023Article
- The Future Is Now: Unraveling the Expanding Potential of Human (Necro)Microbiome in Forensic Investigations.Microorganisms · 2023Review
- Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence.Cureus · 2023Review
- A novel deep learning model for glioma epilepsy associated with the identification of human cytomegalovirus infection injuries based on head MR.Frontiers in microbiology · 2023Article
- Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence.Frontiers in microbiology · 2023Review
- m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.Frontiers in microbiology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.
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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.