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MAMSI:使用多视图机器学习集成多试验液体染色学-质谱代谢学数据

Lukas Kopecky¹, Caroline J Sands², María Gómez-Romero²

¹Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, U.K.

Analytical chemistry

|July 10, 2025

概括

这项研究引入了一个新的工作流程,集成使用多块部分最小平方 (MB-PLS) 模型的多试验代谢学数据. 这种方法可以改善生物标志物发现和数据解释,用于液体染色学-质谱学 (LC-MS) 分析.

科学领域:

代谢学代谢学代谢学
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:

非定位代谢学通常使用液态染色体质谱法 (LC-MS).
广泛的代谢组覆盖通常需要多个分析试验.
目前的方法分别分析测试,阻碍了多测试生物标志物的发现和数据解释.

研究的目的:

开发和评估多试验代谢学数据的综合工作流程.
通过捕捉试验间的关系,使生物标志物发现和阐明未知的代谢物.
改进表型结果预测和数据解释.

主要方法:

提出了一种工作流程,整合了多个测试代谢学数据.
采用多块部分最小平方 (MB-PLS) 模型和多块变量在投影中的重要性.
基于结构性质 (保留时间,质量-电荷比) 的聚类预测因素.
评估了使用三个多项测试数据集来预测生物性别,阿尔茨海默病状态和胆红素水平的方法.

主要成果:

在分类和回归任务中,MB-PLS模型的表现优于单个试验模型.
模拟区块间关系改善了表型结果估计.
确定了潜在的交叉测试生物标志物,结果与现有文献一致.
获得了对每个数据块对预测结果的贡献的见解.

结论:

开发的工作流提供了多试验LC-MS数据的可解释的整合分析.
通过建模试验间的关系来促进潜在生物标志物的发现.
有潜力使更广泛的代谢学社区受益.

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