The current project engineered a machine-learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data.
It 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. (publisher abstract modified)
Downloads
Similar Publications
- Emergency department-based testing for xylazine and other novel psychoactive substances in Central Alabama: a feasibility study
- Organizational Dis trust Comparing Disengagement Among Former Left-Wing and Right-Wing Violent Extremists
- Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates Through Understanding and Predicting Difficulty