Modern machine-learning applications in ambient ionization mass spectrometry

Anatoly A Sorokin; Stanislav I Pekov; Denis S Zavorotnyuk; Mariya M Shamraeva; Denis S Bormotov; Igor A Popov

doi:10.1002/mas.21886

Modern machine-learning applications in ambient ionization mass spectrometry

Mass Spectrom Rev. 2025 Jan-Feb;44(1):74-88. doi: 10.1002/mas.21886. Epub 2024 Apr 26.

Authors

Anatoly A Sorokin¹, Stanislav I Pekov^{2

3

4}, Denis S Zavorotnyuk¹, Mariya M Shamraeva¹, Denis S Bormotov¹, Igor A Popov^{1

3}

Affiliations

¹ Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
² Mass Spectrometry Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia.
³ Translational Medicine Laboratory, Siberian State Medical University, Tomsk, Russia.
⁴ Department for Molecular and Biological Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.

PMID: 38671553
DOI: 10.1002/mas.21886

Abstract

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.

Keywords: ambient ionisation; data analysis; deep learning; machine learning; mass spectrometry imaging.

Publication types

Review

Abstract

Publication types

Grants and funding