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使用MALDI-TOF MS结合长期短期记忆神经网络来识别大肠杆菌菌株

Qiqi Mao¹, Xie Zhang², Zeping Xu²

¹Department of General Surgery, Li Huili Hospital Affiliated to Ningbo University, Ningbo 315040, China.

|July 1, 2024

概括

这项研究引入了一种新的方法,将矩阵辅助激光吸附离子化飞行时间质谱 (MALDI-TOF MS) 与长短期记忆 (LSTM) 神经网络相结合,用于精确的大肠杆菌菌株识别. 综合方法显著提高了比传统方法的准确性.

科学领域:

微生物学微生物学
计算生物学计算生物学
分析化学分析化学

背景情况:

准确识别细菌菌株对于诊断和研究至关重要.
传统的微生物识别方法,包括MALDI-TOF MS,往往难以在菌株层面进行歧视.
机器学习为增强现有技术的分析能力提供了潜力.

研究的目的:

开发和评估一种用于精确识别大肠杆菌菌株的新技术.
整合矩阵辅助激光吸附离子化飞行时间质谱 (MALDI-TOF MS) 与长短期记忆 (LSTM) 神经网络.
与传统方法相比,评估这种结合方法在菌株级别识别方面的有效性.

主要方法:

从48个大肠杆菌菌株中生成了38400个MALDI-TOF MS光谱的综合数据库.
使用了50%的光谱数据来训练精心调整的LSTM神经网络以识别菌株.
在独立测试集上评估模型性能,将结果与传统主要组件分析 (PCA) 进行比较.

主要成果:

传统的PCA未能将48种菌株中的47种分类.
在训练过程中,LSTM神经网络实现了高性能 (损失:0.0524,准确度:0.999,精度:0.985,回忆:0.982).
该模型在未见测试数据上显示出92.24%的强大整体准确性.

结论:

MALDI-TOF MS与LSTM神经网络的整合显著提高了大肠杆菌菌株的识别.
这种创新方法为基于MALDI-TOF MS的菌株水平识别提供了有效和准确的工具.
这项研究扩大了微生物诊断的分析能力,为精确的细菌菌株分化提供了一种强大的方法.

关键词:

埃舍里希亚大肠杆菌 (Escherichia coli) 是一个大肠杆菌.长期短期记忆神经网络神经网络矩阵辅助激光脱吸电离飞行时间质谱法菌株级别的识别识别

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