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Advances in artificial intelligence-based microbiome for PMI estimation
Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regulari...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577360/ https://www.ncbi.nlm.nih.gov/pubmed/36267183 http://dx.doi.org/10.3389/fmicb.2022.1034051 |
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author | Wang, Ziwei Zhang, Fuyuan Wang, Linlin Yuan, Huiya Guan, Dawei Zhao, Rui |
author_facet | Wang, Ziwei Zhang, Fuyuan Wang, Linlin Yuan, Huiya Guan, Dawei Zhao, Rui |
author_sort | Wang, Ziwei |
collection | PubMed |
description | Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation. |
format | Online Article Text |
id | pubmed-9577360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95773602022-10-19 Advances in artificial intelligence-based microbiome for PMI estimation Wang, Ziwei Zhang, Fuyuan Wang, Linlin Yuan, Huiya Guan, Dawei Zhao, Rui Front Microbiol Microbiology Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577360/ /pubmed/36267183 http://dx.doi.org/10.3389/fmicb.2022.1034051 Text en Copyright © 2022 Wang, Zhang, Wang, Yuan, Guan and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Wang, Ziwei Zhang, Fuyuan Wang, Linlin Yuan, Huiya Guan, Dawei Zhao, Rui Advances in artificial intelligence-based microbiome for PMI estimation |
title | Advances in artificial intelligence-based microbiome for PMI estimation |
title_full | Advances in artificial intelligence-based microbiome for PMI estimation |
title_fullStr | Advances in artificial intelligence-based microbiome for PMI estimation |
title_full_unstemmed | Advances in artificial intelligence-based microbiome for PMI estimation |
title_short | Advances in artificial intelligence-based microbiome for PMI estimation |
title_sort | advances in artificial intelligence-based microbiome for pmi estimation |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577360/ https://www.ncbi.nlm.nih.gov/pubmed/36267183 http://dx.doi.org/10.3389/fmicb.2022.1034051 |
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