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洞察差异性移动性谱测中的调剂效应:用于代谢学和性学的数据科学方法

Stepan Stepanovic^1,2, Lysi Ekmekciu¹, Bandar Alghanem^1,3

¹Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland.

Journal of mass spectrometry : JMS

|May 15, 2024

概括

这项研究揭示了影响代谢物和的差分运动谱-质谱 (DMS-MS) 信号抑制的因素. 机器学习模型使用DMS-MS在类分析中识别了影响信号损失的关键分子性质.

科学领域:

分析化学分析化学
生物化学生物化学
频谱测量是一种光谱测量.

背景情况:

差分运动谱-质谱 (DMS-MS) 是分析复杂生物样本的强大技术.
了解影响信号强度的因素对于准确量化和解释DMS-MS数据至关重要.
信号抑制是基于质谱学的分析中常见的挑战,它影响了小分子和.

研究的目的:

调查DMS-MS.中对补偿电压的修饰器效应.
为了识别导致代谢物和中的信号抑制的因素.
在分析中开发信号抑制的预测模型.

主要方法:

采用数据驱动方法来分析DMS-MS数据.
使用统计分析来发现影响小分子信号抑制的因素.
机器学习模型的开发是为了将性质与信号抑制相关联.

主要成果:

确定了导致小分子中信号抑制的特定因素,以及涉及的分析物和机制.
机器学习模型展示了分子重量,拓极地表面积,质电荷和中质子转移诱导的信号抑制之间的关系.
这些模型在预测信号抑制方面表现强.

结论:

这项研究为DMS-MS的信号抑制现象提供了宝贵的见解.
这些发现适用于使用DMS-MS的代谢产物和三性的分析.
开发的模型可以帮助优化DMS-MS应用的实验条件和数据解释.

关键词:

数据分析数据分析数据分析不同的移动性谱测法差异移动性谱测法机器学习是机器学习.代谢产物的代谢产物酸是一种酸.

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