使用可解释的人工智能扩大质谱查询语言的规模
Thomas V Harwood1,2, Mingxun Wang3, Trent R Northen1,2
1Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, California 94720, United States.
Analytical chemistry
|August 25, 2025
概括
一种新的机器学习方法ChemEcho将质谱数据转化为可理解的代谢学规则. 这增强了非目标实验中的结构解释,改善了数据分析和声明.
科学领域:
- 代谢学
- 计算化学
- 生物信息学
背景情况:
- 代谢学数据的解释受阻于从碎片化模式中分配结构信息的挑战.
- 目前用于从质谱数据进行结构预测的AI/ML方法往往缺乏透明度和可解释性.
- 质谱查询语言 (MassQL) 旨在标准化和简化对结构赋值的领域知识的使用.
研究的目的:
- 介绍ChemEcho,一种机器学习嵌入方法,用于将合质谱数据转换为可解释的特征向量.
- 将复杂的AI/ML预测与MassQL中可用的人类可读规则之间的差距缩小.
- 在代谢学中增强可解释的AI/ML应用,以改善结构注释.
主要方法:
- ChemEcho将体质谱数据转换为稀疏特征向量,包括峰值和中性质子公式.
- 使用ChemEcho嵌入式训练决策树来预测分子属性,使其能够直接转换为MassQL查询.
- 为765个分子特征生成了1500多个MassQL查询,并对精度和回忆进行了评估.
主要成果:
- ChemEcho 便于创建直接转换为 MassQL 查询的决策树.
- 50个性能最高的MassQL查询,包括PFAS和具有酸盐/硫酸盐亚结构的分子,被添加到MassQL汇编中.
- 将生成的MassQL查询应用于公开的代谢学数据集,显著增加了来自双重质谱的结构信息.
结论:
- 通过生成人类可读的MassQL查询,ChemEcho提高了人工智能/ML方法的解释性.
- 开发的MassQL查询改善了非目标代谢学实验中的结构注释,从而导致更具体的科学声明.
- 这种方法预计将通过促进更好的数据解释和知识整合,促进代谢学中的各种应用.
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