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• W3099074835 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." @default.
• W3099074835 created "2020-11-23" @default.
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• W3099074835 date "2020-11-09" @default.
• W3099074835 modified "2023-10-18" @default.
• W3099074835 title "Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data" @default.
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• W3099074835 doi "https://doi.org/10.1038/s41587-020-0700-3" @default.
• W3099074835 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7971188" @default.
• W3099074835 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33169034" @default.
• W3099074835 hasPublicationYear "2020" @default.
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