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Xu G, Teng X, Gao XH, Zhang L, Yan H, Qi RQ. Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence. Front Microbiol 2023; 14:1332857. [PMID: 38179452 PMCID: PMC10764511 DOI: 10.3389/fmicb.2023.1332857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.
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Affiliation(s)
- Geyao Xu
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Xianzhuo Teng
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xing-Hua Gao
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Li Zhang
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Hongwei Yan
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Rui-Qun Qi
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
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