| Literature DB >> 33169034 |
Alexander A Aksenov1,2, Ivan Laponogov3, Zheng Zhang1, Sophie L F Doran3, Ilaria Belluomo3, Dennis Veselkov4,5, Wout Bittremieux1,2,6, Louis Felix Nothias1,2, Mélissa Nothias-Esposito1,2, Katherine N Maloney1,7, Biswapriya B Misra8, Alexey V Melnik1, Aleksandr Smirnov9, Xiuxia Du9, Kenneth L Jones1, Kathleen Dorrestein1,2, Morgan Panitchpakdi1, Madeleine Ernst1,10, Justin J J van der Hooft1,11, Mabel Gonzalez12, Chiara Carazzone12, Adolfo Amézquita13, Chris Callewaert14,15, James T Morton15,16, Robert A Quinn17, Amina Bouslimani1,2, Andrea Albarracín Orio18, Daniel Petras1,2, Andrea M Smania19,20, Sneha P Couvillion21, Meagan C Burnet21, Carrie D Nicora21, Erika Zink21, Thomas O Metz21, Viatcheslav Artaev22, Elizabeth Humston-Fulmer22, Rachel Gregor23, Michael M Meijler23, Itzhak Mizrahi24, Stav Eyal24, Brooke Anderson25, Rachel Dutton25, Raphaël Lugan26, Pauline Le Boulch26, Yann Guitton27, Stephanie Prevost27, Audrey Poirier27, Gaud Dervilly27, Bruno Le Bizec27, Aaron Fait28, Noga Sikron Persi28, Chao Song28, Kelem Gashu28, Roxana Coras29, Monica Guma29, Julia Manasson30, Jose U Scher30, Dinesh Kumar Barupal31, Saleh Alseekh32,33, Alisdair R Fernie32,33, Reza Mirnezami34, Vasilis Vasiliou35, Robin Schmid36, Roman S Borisov37, Larisa N Kulikova38, Rob Knight15,39,40,41, Mingxun Wang1,2, George B Hanna3, Pieter C Dorrestein42,43,44,45, Kirill Veselkov46.
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.Entities:
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Year: 2020 PMID： 33169034 PMCID： PMC7971188 DOI： 10.1038/s41587-020-0700-3
Source DB: PubMed Journal: Nat Biotechnol ISSN： 1087-0156 Impact factor: 54.908