| 江嘉思,张文骥,黎子华,易梓琛.化学通报,2026,89(2):215-222. |
| 基于机器学习的代谢组学在推断死亡时间的研究 |
| Research on Estimating Postmortem Interval Based on Machine Learning and Metabolomics |
| 投稿时间：2025-05-29 修订日期：2025-09-02 |
| DOI： |
| 中文关键词: PMI推断 法庭科学 机器学习 代谢组学 小分子代谢物 |
| 英文关键词:PMI inference, forensic science, machine learning, metabolomics, small molecule metabolites |
| 基金项目:辽宁省自然科学基金计划项目(2024-MS-208)、沈阳市中青年科技人才培育专项(RC240610)、辽宁省科技厅重点研发项目(2024JH2/102500079)和中国刑事警察学院研究生创新能力提升项目(2024YCYB47)资助 |
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| 中文摘要: |
| 死亡时间（Postmortem Interval, PMI）推断是法庭科学领域的热点和难题，对于案件侦破和司法鉴定具有重要意义。传统的PMI推断方法（尸体现象及昆虫群落演替现象等）易受环境因素和PMI长短的干扰，不能做到精准预测。以机器学习为核心的代谢组学技术通过分析死后生物体内小分子代谢物随PMI的变化，在PMI推断研究中展现出巨大潜力。本文综述了近十年来国内外代谢组学技术结合机器学习在PMI推断中的研究进展，涵盖研究流程、不同组织和死因代谢物变化与PMI的关系，为法庭科学提供兼具高精度与准确度的分析工具。 |
| 英文摘要: |
| The estimation of postmortem interval (PMI) constitutes a pivotal yet challenging domain within forensic science, bearing significant implications for criminal investigations and judicial identification. Traditional PMI estimation methodologies, such as cadaveric phenomena and insect succession patterns, are susceptible to environmental variables and PMI duration, thereby lacking precision in predictive capabilities. Metabolomics technology, underpinned by machine learning algorithms, has demonstrated substantial potential in PMI estimation research through the analysis of small molecule metabolite alterations in postmortem biological specimens. This paper provides a comprehensive review of the advancements in metabolomics technology integrated with machine learning for PMI estimation over the past decade, encompassing research protocols, the relationship between metabolite changes in various tissues and causes of death with PMI, thereby offering forensic science an analytical tool characterized by both high precision and legal credibility. |
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