Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation

doi:10.12116/j.issn.1004-5619.2022.421105

Abstract

Abstract:

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

Key words: forensic medicine, metabolomics, machine learning, wound age estimation, review

CLC Number:

Xing-yu MA, Hao CHENG, Zhong-duo ZHANG, Ye-ming LI, Dong ZHAO. Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation[J]. Journal of Forensic Medicine, 2023, 39(6): 596-600.

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