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)
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- Assessment of Neighborhood Poverty, Cognitive Function, and Prefrontal and Hippocampal Volumes in Children
- Platinum-Quality Mitogenome Haplotypes from United States Populations