MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data

<b>Background</b>: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development o...

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Bibliographic Details
Main Authors:	Nami Sakamoto, Takaki Oka, Yuki Matsuzawa, Kozo Nishida, Jayashankar Jayaprakash, Aya Hori, Makoto Arita, Hiroshi Tsugawa
Format:	Article
Language:	English
Published:	MDPI AG 2024-11-01
Series:	Metabolites
Subjects:	untargeted lipidomics tandem mass spectrum machine learning lipid class prediction microbiota-dependent lipids human fecal samples
Online Access:	https://www.mdpi.com/2218-1989/14/11/602
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MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data

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