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将流量病毒计与基于树的机器学习模型相结合,用于在不同的废水矩阵中快速估计病毒粒子

Yevhen Myshkevych¹, Ibrahima N'Doye², Julie Sanchez Medina³

¹Environmental Science and Engineering Program, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia; KAUST Center of Excellence on Smart Health, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.

|June 7, 2025

概括

流病毒计 (FVM) 和机器学习 (ML) 准确估计废水中的病毒颗粒 (VP) 度. 这种综合方法通过优化各种水矩阵中的VP计数来增强公共卫生监测.

科学领域:

环境微生物学环境微生物学
分析化学是一种分析化学.
计算生物学是一种计算生物学.

背景情况:

准确的病毒颗粒 (VPs) 计数对于废水处理效率和公共卫生保护至关重要.
现有的VP检测方法可能是劳动密集型的,可能不适合各种废水矩阵.

研究的目的:

优化流量病毒计 (FVM) 用于废水分析.
将FVM数据与特定病毒类度进行相关联.
开发机器学习 (ML) 模型来预测废水中的总VP度.

主要方法:

测试了FVM的各种样品预处理技术,包括染色剂,表面活性剂和固定剂.
对五种病毒基因进行了FVM和qPCR数据之间的斯皮尔曼等级相关性.
开发并比较极端梯度增强 (XGB) 和随机森林 (RF) ML模型,使用物理化学水参数来预测VP度.

主要成果:

优化的FVM协议证明了废水中检测灵敏度的提高.
FVM数据显示了与五个目标病毒属的度的正相关性 (斯皮尔曼的rho:0.210.44,p <0.01).
XGB模型显著优于RF模型,在估计不同废水处理阶段的总VP度时,实现了23%较低的根平均平方误差.

结论:

流病毒计 (FVM),当优化并与机器学习 (ML) 结合时,为废水中的病毒颗粒计数提供了一个强大的方法.
ML模型,特别是XGB,可以使用易于获得的水质参数准确估计VP总度.
这种综合方法提高了废水监测能力,有助于改善公共卫生风险评估.

关键词:

流量病毒计流量病毒计.机器学习是机器学习.病毒编号编号病毒编号编号重新利用水的方法

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