现代机器学习在环境电离质谱学中的应用
Anatoly A Sorokin1, Stanislav I Pekov2,3,4, Denis S Zavorotnyuk1
1Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Mass spectrometry reviews
|April 27, 2024
概括
机器学习 (ML) 和人工智能 (AI) 正在彻底改变环境电离质谱 (AIMS). 这些技术增强了数据分析的快速和敏感的样本分析,有利于AIMS社区.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 环境电离质谱 (AIMS) 提供快速,灵敏的分析与最小的样本准备.
- 越来越复杂的AIMS数据需要先进的分析方法.
研究的目的:
- 在AIMS.中提供机器学习 (ML) 和人工智能 (AI) 应用的全面概述.
- 突出 ML/AI 与 AIMS 整合的进步和好处.
主要方法:
- 对适用于AIMS数据的ML/AI算法的审查.
- 讨论该领域的关键进展.
主要成果:
- 机器学习/人工智能集成显著提高了AIMS中的数据分析能力.
- 不同的ML/AI算法适用于处理AIMS数据集.
结论:
- 机器学习和人工智能是推动AIMS的强大工具.
- ML/AI和AIMS之间的协同作用有望在质谱学领域进一步创新.
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