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基于机器学习的分类方法的应用,用于开发宿主蛋白对传染病的诊断模型

Thomas F Scherr¹, Christina E Douglas², Kurt E Schaecher³

¹Atticus Labs, Baltimore, MD 21212, USA.

Diagnostics (Basel, Switzerland)

|June 27, 2024

概括

本研究介绍了一种机器学习 (ML) 工作流程,用于使用宿主蛋白生物标志物对传染病进行分类. 开发的模型准确地区分了细菌,病毒和正常样本,提供了一种新的诊断方法.

科学领域:

生物医学诊断生物医学诊断
计算生物学是一种计算生物学.
传染病研究传染病研究.

背景情况:

传染病诊断传统上依赖于病原体检测.
以主机为中心的方法提供了互补的见解,但需要复杂的数据解释.
机器学习 (ML) 可能会简化对宿主生物标志物数据的分析.

研究的目的:

开发和展示基于ML的分类工作流程的模板,用于以宿主为中心的传染病诊断.
使用宿主蛋白质生物标志物构建和优化一种ML模型,用于区分细菌,病毒和非疾病状态.
为了证明自动化ML (Auto-ML) 方法用于识别诊断生物标记符号的实用性.

主要方法:

从已知疾病病因 (细菌,病毒,正常) 的样本中收集人血清蛋白质数据.
采用自动机器学习 (Auto-ML) 策略来训练和优化分类模型.
在一组盲目样本上验证了优化分类器的性能.

主要成果:

一个优化的Auto-ML分类模型成功地区分了细菌,病毒和正常人血清样本.
该模型表现出强大的诊断特征,即使在有限的培训数据集大小.
当该模型应用于独立的盲目样本组时,观察到有效的性能.

结论:

提出的Auto-ML工作流提供了一种灵活和可适应的方法,用于开发宿主传染病生物标志物分类器.
这种方法可以帮助研究人员识别各种疾病状态和生物标志物类别的基于宿主的诊断特征.
以宿主为中心的ML模型显示出对补充传统的病原体导向诊断方法的承诺.

关键词:

生物标志物生物标志物这是分类分类的分类.诊断诊断诊断的诊断诊断诊断诊断的诊断主机的主机主机的主机主机传染病是一种传染性疾病.机器学习是机器学习.蛋白质蛋白质是蛋白质蛋白质的组成部分.

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