NCJ Number
303983
Journal
Nature Biotechnology Volume: 39 Issue: 2 Dated: 2021
Date Published
2021
Annotation
The current project engineered a machine-learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data.
Abstract
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)
Date Published: January 1, 2021
Downloads
Similar Publications
- Evaluating Side Channel Resilience in iPhone 5c Unlock Scenarios
- CPU HARDWARE CLASSIFICATION AND PERFORMANCE PREDICTION USING NEURAL NETWORKS AND STATISTICAL LEARNING
- Application of X-ray photoelectron spectroscopy to examine surface chemistry of cancellous bone and medullary contents to refine bone sample selection for nuclear DNA analysis