Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data
Abstract
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via...
Paper Details
Title
Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data
Published Date
Nov 9, 2020
Journal
Volume
39
Issue
2
Pages
169 - 173
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