mzLearn作为一个数据驱动的LC/MS信号检测算法,它允许预训练的生成模型用于非目标代谢学
Leila Pirhaji1, Jonah Eaton2, Adarsh K Jeewajee2
1ReviveMed Inc, Cambridge, MA, USA. lpirhaji@revivemed.io.
Communications chemistry
|December 18, 2025
概括
mzLearn通过改善代谢物信号检测和对齐来增强非目标代谢. 这种数据驱动的方法能够对大型数据集进行可靠的分析,为疾病研究中先进的生成模型铺平了道路.
科学领域:
- 代谢学 代谢学 代谢学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 代谢物改变是疾病的关键生物标志物.
- 大规模的非目标代谢学面临信号检测和数据整合的挑战.
- 开发预训练的生成模型需要高质量的,集成的数据集.
研究的目的:
- 介绍mzLearn,一种用于MS1信号检测和对齐的新型数据驱动方法.
- 克服当前代谢学数据处理中的局限性.
- 允许开发预训练的基础模型,用于非目标代谢学.
主要方法:
- mzLearn 在没有用户设置参数的情况下处理 mzML 文件.
- 它采用数据驱动的方法来进行信号检测和对齐.
- 该方法在15个公共数据集和22项涉及20548个血液样本的研究中得到了验证.
主要成果:
- mzLearn检测到显著更多的信号 (11,442个月). 与XCMS (7,100) 和ASARI (4,655) 相比,它们的数量更高.
- 实现了更高的真实阳性 (89.0%) 和更低的虚假阳性 (12.5%) 率.
- 实现了预先训练的变异性自编码器的开发,改善了细胞癌风险分层和生存预测.
结论:
- mzLearn提供了一个强大的,可扩展的解决方案,用于MS1特征检测和调整在非目标代谢学.
- 便于创建基础模型所必需的高质量的特征矩阵.
- 促进了代谢学在疾病研究和生物标志物发现中的应用.
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