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Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971188 | PMC |
| http://dx.doi.org/10.1038/s41587-020-0700-3 | DOI Listing |
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