Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data
- 8 November 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Biotechnology
- Vol. 39 (2), 169-173
- https://doi.org/10.1038/s41587-020-0700-3
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 unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples. A machine learning workflow enables auto-deconvolution of gas chromatography-mass spectrometry data.This publication has 20 references indexed in Scilit:
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