Modern machine-learning applications in ambient ionization mass spectrometry

被引：2

作者：

Sorokin, Anatoly A. ^{[1
]}

Pekov, Stanislav I. ^{[2
,3
,4
]}

Zavorotnyuk, Denis S. ^{[1
]}

Shamraeva, Mariya M. ^{[1
]}

Bormotov, Denis S. ^{[1
]}

Popov, Igor A. ^{[1
,3
,5
]}

机构：

[1] Moscow Inst Phys & Technol, Lab Mol Med Diag, Dolgoprudnyi, Russia

[2] Skolkovo Inst Sci & Technol, Mass Spectrometry Lab, Moscow, Russia

[3] Siberian State Med Univ, Translat Med Lab, Tomsk, Russia

[4] Moscow Inst Phys & Technol, Dept Mol & Biol Phys, Dolgoprudnyi, Russia

[5] Moscow Inst Phys & Technol, Lab Mol Med Diag, Dolgoprudnyi 141701, Russia

来源：

MASS SPECTROMETRY REVIEWS | 2025年 / 44卷 / 01期

关键词：

ambient ionisation; data analysis; deep learning; machine learning; mass spectrometry imaging; PAPER SPRAY; IN-VIVO; CANCER; QUANTIFICATION; NORMALIZATION; RESOLUTION; VERSATILE; CHEMISTRY; GEOMETRY; MIXTURES;

D O I：

10.1002/mas.21886

中图分类号：

O433 [光谱学];

学科分类号：

0703 ; 070302 ;

摘要：

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

引用

页码：74 / 88

页数：15

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