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Würf, V.* ; Köhler, N.* ; Molnar, F.* ; Hahnefeld, L.* ; Gurke, R.* ; Witting, M. ; Pauling, J.K.*

LipiDetective:a deep learning model for the identification of molecular lipid species in tandem mass spectra.

Brief. Bioinform. 27:bbag378 (2026)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
Confidently identifying lipids in samples is a prerequisite for understanding their many functions in health and disease. However, accurate molecular lipid species identification via tandem mass spectrometry remains challenging. Most current approaches match measured spectra against an in-house reference library, which hinders the comparability of results. To address this challenge, the transformer model LipiDetective was developed and trained on a dataset of spectra from lipid standards, databases, and publications. Learning the characteristic lipid fragmentation patterns, LipiDetective can accurately annotate molecular lipid species in tandem mass spectra independently of the experimental setup. Integrated gradients reveals that LipiDetective focuses on peaks matching known fragments, making its predictions humanly interpretable. Therefore, LipiDetective offers a data-driven approach for molecular lipid species identification that may improve the comparability of annotations across different laboratories and analysis workflows.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Deep Learning ; Lipid Identification ; Lipidomics ; Mass Spectrometry ; Transformer Neural Network; Spectrometry
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Zeitschrift Briefings in Bioinformatics
Quellenangaben Band: 27, Heft: 4, Seiten: , Artikelnummer: bbag378 Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Begutachtungsstatus Peer reviewed
Institut(e) Core Facility Metabolomics and Proteomics (CF-MPC)
Förderungen Bavarian State Ministry of Science and the Arts in the framework of the Bavarian Research Institute for Digital Transformation
Deutsche Forschungsgemeinschaft