|
Title
|
|
|
|
Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data
| |
|
Author
|
|
|
|
| |
|
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. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
Nature biotechnology. - New York, N.Y., 1996, currens
| |
|
Publication
|
|
|
|
Berlin
:
Nature research
,
2021
| |
|
ISSN
|
|
|
|
1087-0156
| |
|
DOI
|
|
|
|
10.1038/S41587-020-0700-3
| |
|
Volume/pages
|
|
|
|
39
:2
(2021)
, p. 169-173
| |
|
ISI
|
|
|
|
000588003500002
| |
|
Pubmed ID
|
|
|
|
33169034
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (open access)
|
|
|
|
| |
|