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
- The Study of Tissue-Specific DNA Methylation as a Method for the Epigenetic Discrimination of Forensic Samples
- Targeted-Ion Mass Spectrometry for the Identification of Forensically Relevant Biological Fluids and Samples from Sexual Assault Evidence
- Physical and Biochemical Factors Affecting the Recovery and Analysis of DNA from Human Skeletal Remains