PDF(Pubmed)
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.
摘要：
当面对一个身份不明的尸体时，识别受害者可能很有挑战性,特别是如果物理特性被掩盖或掩盖。近年来,法医科学中的微生物分析已成为一项尖端技术。它不仅表现出个体特异性，区分不同的人类生物品种与不同的发生地点(例如，胃肠,口服,皮肤,呼吸,和泌尿生殖道)，每个都有不同的细菌物种，而且还提供了对事故地点和周围环境的见解。与传统测序技术相比，机器学习与微生物的集成在对细菌物种进行分类方面提供了实质性改进。这篇综述讨论了机器学习算法的使用，如RF、SVM,ANN,DNN,回归，和BN用于检测和鉴定各种细菌，包括炭疽杆菌,醋酸杆菌,金黄色葡萄球菌,和链球菌,在其他人中。深度学习技术，如卷积神经网络(CNN)模型和导数，还被用来预测受害者的年龄，性别,生活方式,和种族特征。预计大数据分析和人工智能将在未来推进法医微生物学方面发挥关键作用。
