针对生物信息学工具,研究抗生素耐药基因在环境和临床环境中的传播和传播
Chandra Kant Singh1, Kushneet Kaur Sodhi2
1Department of Zoology, University of Delhi, Delhi, India.
Critical reviews in microbiology
|November 18, 2024
概括
由于过度使用,抗生素耐药性正在蔓延. 生物信息学,元基因组学和机器学习是监测和打击抗生素耐药性基因 (ARG) 和抗菌素耐药性 (AMR) 的关键工具.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 遗传学 是一个遗传学.
背景情况:
- 抗生素耐药性基因 (ARG) 通过横向基因转移 (HGT) 传播,在医疗,食品和农业部门通过不小心使用抗生素来促进传播.
- 抗菌素耐药性 (AMR) 构成了全球健康的重大威胁,需要先进的监测策略.
研究的目的:
- 审查生物信息学工具的应用,包括超基因组学和机器学习 (ML),用于监测抗生素耐药性.
- 突出先进的计算方法在了解和打击AMR中的作用.
主要方法:
- 在不同的环境中对抗体进行分类的元基因组和元转录组技术.
- 使用高通量测序对ARG序列的分析.
- 机器学习 (ML) 和人工智能 (AI) 算法对抗药物反应数据的应用.
主要成果:
- 转基因组学和转基因转录组学是识别微生物群落中ARG的先进工具.
- 基于计算进步,ML算法越来越多地用于分析大规模的AMR数据.
结论:
- 生物信息学,特别是元基因组学和ML/AI,对于有效的抗生素耐药性监测和管理至关重要.
- 未来对抗AMR的战略将在很大程度上依赖于AI和ML来预测化合物相互作用和优化治疗.
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