通过基于毒性基因的机器学习方法在土壤中识别人类病原体
Shengchun Qi1, Shuyan Wang1, Yu Xia2
1State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou 310058, China.
Eco-Environment & Health
|August 18, 2025
概括
一种新的机器学习方法,基于毒性因子 (VF) 的K-最近邻居 (VF-KNN),准确地识别了土壤元基因组中的人类致病细菌. 这种方法提高了病原体的检测,并揭示了农业土壤中更多的丰度.
科学领域:
- 环境微生物学环境微生物学
- 生物信息学是一种生物信息学.
- 机器学习在病原体检测中的应用.
背景情况:
- 土壤含有致病性人类细菌,构成公共卫生风险.
- 超基因组学提供了病原体识别,但面临着诸如耗时组装和依赖参考数据库等局限性.
- 现有的方法可能会错过新的或未表征的病原体.
研究的目的:
- 开发和验证一种新的机器学习方法,用于识别土壤元基因组中的人类病原细菌.
- 利用毒性因子 (VFs) 提高病原体检测的准确性和范围.
- 评估中国不同土地类型的土壤病原体的数量和分布.
主要方法:
- 开发了一个基于毒性因子 (VF) 的K-最近邻居 (VF-KNN) 机器学习模型.
- 在致病性和非致病性细菌的VF特征上训练模型.
- 使用土壤元基因组数据和分离的致病菌株验证了模型,评估了准确性和基因组覆盖率.
主要成果:
- VF-KNN在土壤病原体识别方面取得了高性能 (AUC:0.95,准确度:0.85),在孤立菌株上以0.95准确度验证.
- 该模型在0.4X-1.0X基因组覆盖率下显示了顶部土壤病原体的>0.90预测准确度.
- 与传统方法相比,VF-KNN发现了28%更多的潜在病原体物种,包括最近报告的如*Mycolicibacterium cosmeticum*.
结论:
- VF-KNN方法提供了一种强大而有效的方法,用于在土壤元基因组中识别人类致病细菌.
- 这种方法扩大了潜在病原体的检测范围,超出了预定义的列表和参考基因组.
- 土壤病原体在农业用地更为丰富和多样化,在中国东部的地表土壤中存在显著的存在.
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