基于MALDI-TOF MS和机器学习的Bifidobacterium longum亚种的快速歧视
Kexin Liu1,2, Yajie Wang3, Minlei Zhao4
1College of Life Science, North China University of Science and Technology, Tangshan, China.
Frontiers in microbiology
|December 19, 2023
概括
矩阵辅助激光脱/离子化飞行时间质谱学 (MALDI-TOF MS) 结合机器学习准确区分Bifidobacterium longum亚种infantis和longum. 这种方法为益生菌和药品提供了快速而精确的亚种识别.
科学领域:
- 微生物学 微生物学
- 分析化学 分析化学
- 生物信息学是一种生物信息学.
背景情况:
- MALDI-TOF MS是微生物识别的标准,但难以区分密切相关的Bifidobacterium亚种.
- 商业数据库缺乏在Bifidobacterium中精确的亚种级别歧视的分辨率.
研究的目的:
- 开发一种方法来区分Bifidobacterium longum亚种. 婴儿菌 (B. infantis) 和Bifidobacterium longum亚种. 这两种细菌都存在. 长 (B.长) 使用MALDI-TOFMS.
- 探索机器学习算法的实用性,用于分析MALDI-TOF MS蛋白质配置文件,用于分种分类.
主要方法:
- 从59个B. longum和41个B. infantis菌株的质谱分析.
- 使用反复特征消除 (RFE) 识别了五个关键生物标志物峰值 (m/z 2,929,4,408,5,381,5,394,8,817).
- 开发和评估机器学习模型 (逻辑回归,随机森林,支持矢量机) 用于分类.
主要成果:
- 随机森林 (RF) 模型实现了最高的性能,曲线下的面积 (AUC) 为0.984.
- 与其他算法相比,RF模型显示出更高的精度和灵敏度.
- 使用多重质谱数据与射频模型进行的投票机制为菌株识别提供了96.67%的准确性.
结论:
- MALDI-TOF MS与机器学习相结合,提供了一个强大的方法来区分B. longum和B. infantis亚种.
- 这种方法在益生菌和制药行业具有重要的商业应用潜力.
- 精确的亚种区分对于相关部门的产品开发和质量控制至关重要.
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