先进的数据分析和"omics"技术用于控制肠道食品传播病原体
Shraddha Karanth1, Abani K Pradhan2
1Department of Nutrition and Food Science, University of Maryland, College Park, MD, United States.
Advances in food and nutrition research
|March 1, 2025
概括
先进的数据分析,包括机器学习,对于分析肠道病原体的omics数据至关重要. 这种方法有助于减轻食品安全风险,并保护公众健康免受食物传播疾病的影响.
科学领域:
- 食品安全 食品安全
- 微生物学 微生物学
- 数据科学数据科学数据科学
背景情况:
- 肠道细菌病原体导致全球数百万种食源性疾病.
- 诸如大肠杆菌,李斯特菌和沙门氏菌等病原体的亚型表现出显著的差异.
- 传统的方法很难跟上病原体行为的复杂性.
研究的目的:
- 审查先进数据分析的应用,特别是机器学习,用于分析肠道细菌病原体的奥米克数据.
- 突出这些技术在提高食品安全和公共卫生方面的潜力.
- 确定这个领域的未来研究方向.
主要方法:
- 使用"奥米克"技术 (基因组学,蛋白质组学,转录组学,代谢组学) 来收集病原体数据.
- 应用先进的数据分析,包括机器学习和人工智能,用于数据解释.
- 审查关于肠道病原体数据分析策略的当前研究.
主要成果:
- 奥米克斯数据为肠道病原体的遗传和表型差异提供了深入的见解.
- 机器学习和人工智能越来越有效地从复杂的病原体数据集中提取可操作的知识.
- 这些先进的分析技术有助于预测和控制食品系统中病原体的流行.
结论:
- 先进的数据分析,特别是机器学习,对于解释肠道病原体的OMIC数据至关重要.
- 这些方法为减轻食品安全风险和保护公共健康提供了强大的工具.
- 这些技术的持续开发和应用对于未来的食品安全战略至关重要.
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