Kitchens Steven Ray, Wang Chengming, Price Stuart B
Department of Pathobiology, College of Veterinary Medicine, Auburn University, 1130 Wire Road, Auburn, AL 36849-5519, USA.
Microorganisms. 2024 Nov 7;12(11):2249. doi: 10.3390/microorganisms12112249.
Advancements in genomics and machine learning have significantly enhanced the study of epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with , offers additional layers of understanding. Phages can impact population dynamics and evolution, and their integration into genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of .
基因组学和机器学习的进展显著加强了流行病学研究。全基因组测序彻底改变了细菌基因组学，使得对基因变异进行详细分析成为可能，并有助于疫情调查和源头追踪。短读长测序技术，如Illumina公司提供的技术，在生成便于血清分型和检测抗菌药物耐药性的基因组草图方面发挥了重要作用。长读长测序技术，包括太平洋生物科学公司和牛津纳米孔技术公司的技术，为更完整的基因组组装以及更深入了解遗传多样性提供了潜力。除了这些测序方法外，决策树和随机森林等机器学习技术为模式识别和预测建模提供了强大工具。重要的是，对与[具体对象]相互作用的噬菌体的研究提供了更多层面的理解。噬菌体可影响[具体对象]种群动态和进化，将其纳入[具体对象]基因组学研究有望为病原体控制和流行病学带来新的见解。本综述回顾了[具体对象]的历史及其发病机制，并强调了这些现代方法在增进我们对[具体对象]理解方面的整合。
