Реферат: Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a “dream tool” for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

Библиографическая ссылка: Boiko D.A. , Kozlov K.S. , Burykina J.V. , Ilyushenkova V.V. , Ananikov V.P.
Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning
Journal of the American Chemical Society. 2022. V.144. N32. P.14590-14606. DOI: 10.1021/jacs.2c03631 WOS Scopus OpenAlex

Идентификаторы БД:

Web of science:	WOS:000839465900001
Scopus:	2-s2.0-85136909079
OpenAlex:	W4290725197

Цитирование в БД:

БД	Цитирований
OpenAlex	175
Scopus	35
Web of science	25

Альметрики: