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Predicting postmortem interval based on microbial community sequences and machine learning algorithms
Author(s) -
Liu Ruina,
Gu Yuexi,
Shen Mingwang,
Li Huan,
Zhang Kai,
Wang Qi,
Wei Xin,
Zhang Haohui,
Wu Di,
Yu Kai,
Cai Wumin,
Wang Gongji,
Zhang Siruo,
Sun Qinru,
Huang Ping,
Wang Zhenyuan
Publication year - 2020
Publication title -
environmental microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.954
H-Index - 188
eISSN - 1462-2920
pISSN - 1462-2912
DOI - 10.1111/1462-2920.15000
Subject(s) - biology , microbial population biology , decomposition , artificial intelligence , process (computing) , algorithm , support vector machine , microbiome , enterococcus faecalis , artificial neural network , interval (graph theory) , random forest , lactobacillus reuteri , machine learning , ecology , computer science , bioinformatics , bacteria , lactobacillus , mathematics , paleontology , combinatorics , staphylococcus aureus , operating system
Summary Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis , Anaerosalibacter bizertensis , Lactobacillus reuteri , and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24‐h decomposition and 14.5 ± 4.4 h within 15‐day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.