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Royal Society of Chemistry, Faraday Discussions, (218), p. 284-302, 2019

DOI: 10.1039/c8fd00235e

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Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Journal article published in 2019 by Simon Rogers ORCID, Cher Wei Ong, Joe Wandy ORCID, Madeleine Ernst ORCID, Lars Ridder ORCID, Justin J. J. van der Hooft ORCID
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Integration of MS2LDA substructure discovery with MAGMa spectral annotations and ClassyFire term predictions complemented with MotifDB significantly advances metabolite annotation.