Predicting postmortem interval based on microbial community sequences and machine learning algorithms-Reference-Cited by-同舟云学术

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Published:2020-04-05 Issue:6 Volume:22 Page:2273-2291
ISSN:1462-2912
Container-title:Environmental Microbiology
language:en
Short-container-title:Environ Microbiol

Author:

Liu Ruina¹,Gu Yuexi²,Shen Mingwang³,Li Huan⁴,Zhang Kai¹,Wang Qi⁵,Wei Xin¹,Zhang Haohui¹,Wu Di¹,Yu Kai¹,Cai Wumin¹,Wang Gongji¹,Zhang Siruo⁴,Sun Qinru¹,Huang Ping⁶,Wang Zhenyuan¹^ORCID

Affiliation:

1. College of Forensic Medicine, Xi'an Jiaotong University Xi'an 710061 China

2. School of Mathematics and Statistics, Xi'an Jiaotong University Xi'an 710061 China

3. Department of Epidemiology and Biostatistics School of Public Health, Xi'an Jiaotong University Health Science Center Xi'an Shaanxi 710061 China

4. Department of Microbiology and immunology School of Basic Medical Sciences, Xi'an Jiaotong University Xi'an China

5. Chongqing Medical University, College of Basic Medicine Department of Forensic Medicine Chongqing 400016 China

6. Shanghai Key Laboratory of Forensic Medicine Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice Shanghai 200063 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics,Microbiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1462-2920.15000

Reference65 articles.

1. Alan G. andSarah J.P.J.T.B.(2012) Microbes as forensic indicators.29:311.

2. Empirical characterization of random forest variable importance measures

3. Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

4. The Potential of High-throughput Metagenomic Sequencing of Aquatic Bacterial Communities to Estimate the Postmortem Submersion Interval

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