基于使用随机森林模型进行基因组分析,预测Listeria monocytogenes的食物来源
Weidong Gu1, Zhaohui Cui1, Steven Stroika1
1Division of Foodborne, Waterborne and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Foodborne pathogens and disease
|September 12, 2023
概括
机器学习模型现在可以使用全基因组多部位序列类型数据预测Listeria monocytogenes感染的食物来源. 这一进步有助于识别爆发未被识别时零星的食物传播疾病的起源.
科学领域:
- 食品传播疾病的监测监测
- 分子流行病学分子流行病学
- 机器学习在公共卫生中的应用.
背景情况:
- 李斯特菌 (Listeria monocytogenes) 引起严重的食物传播疾病,零星病例往往缺乏确定的食物来源.
- 目前的分子监测主要依赖于全基因组多部位序列类型化 (wgMLST),主要用于疫情检测.
研究的目的:
- 为了研究机器学习模型的实用性,用于归因Listeria monocytogenes分离的食物来源.
- 评估随机森林模型的性能,使用wgMLST等位基因数据进行来源归因.
主要方法:
- 从PulseNet数据库中编译出具有已知的食物来源 (乳制品,水果,肉类,海鲜,蔬菜) 的Listeria monocytogenes分离物.
- 将随机森林机器学习模型应用于wgMLST等位基因数据,以预测孤立的食物来源.
- 在不同食品类别中评估模型预测准确度.
主要成果:
- 随机森林模型实现了49%的整体预测准确度,明显超过了天真预测准确度 (28%).
- 预测准确度因食品类别而异,肉类的预测准确度最高 (65%),海鲜的预测准确度最低 (37%).
结论:
- 机器学习,特别是随机森林模型,可以有效地利用高分辨率的wgMLST数据来归因Listeria monocytogenes的来源.
- 这种方法为识别零星食物传播疾病的食物来源提供了一个有希望的工具,增强公共卫生干预措施.
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