Modern machine‐learning applications in ambient ionization mass spectrometry-Reference-Cited by-同舟云学术

Modern machine‐learning applications in ambient ionization mass spectrometry

Published:2024-04-26 Issue: Volume: Page:
ISSN:0277-7037
Container-title:Mass Spectrometry Reviews
language:en
Short-container-title:Mass Spectrometry Reviews

Author:

Sorokin Anatoly A.¹,Pekov Stanislav I.²³⁴^ORCID,Zavorotnyuk Denis S.¹,Shamraeva Mariya M.¹,Bormotov Denis S.¹,Popov Igor A.¹³^ORCID

Affiliation:

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

Abstract

AbstractThis 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.

Funder

Ministry of Science and Higher Education of the Russian Federation

Publisher

Wiley

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