Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

Li, Yuanyue; Kuhn, Michael; Gavin Perrin, Anne-Claude; Bork, Peer

doi:10.1093/bioinformatics/btz736

Scientific article

English

Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

ContributorsLi, Yuanyue; Kuhn, Michael; Gavin Perrin, Anne-Claude; Bork, Peer

Published inBioinformatics

Publication date2019

Abstract

Untargeted mass spectrometry is a powerful method for detecting metabolites in biological samples. However, fast and accurate identification of the metabolites' structures from MS/MS spectra is still a great challenge.

Affiliation entities

Not a UNIGE publication

Research groups

Métabolisme des lipides (1001)

Citation (ISO format)

LI, Yuanyue et al. Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features. In: Bioinformatics, 2019. doi: 10.1093/bioinformatics/btz736

Article (Published version)

CC BY

Identifiers

PID : unige:124756
DOI : 10.1093/bioinformatics/btz736
PMID : 31605112

Journal ISSN1367-4803

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595downloads

Creation22/10/2019 13:27:00

First validation22/10/2019 13:27:00

Update13/10/2025 17:57:15

Status update15/03/2023 18:13:41

Last indexation16/01/2026 14:25:04

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Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

Untargeted mass spectrometry is a powerful method for detecting metabolites in biological samples. However, fast and accurate identification of the metabolites' structures from MS/MS spectra is still a great challenge.

Technical informations