Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA.
Nat Biotechnol. 2021 Feb;39(2):169-173. doi: 10.1038/s41587-020-0700-3. Epub 2020 Nov 9.
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.
我们设计了一种机器学习方法 MSHub，以实现气相色谱-质谱（GC-MS）数据的自动解卷积。然后，我们设计了工作流程，使社区能够在全球天然产物社会（GNPS）分子网络分析平台内存储、处理、共享、注释、比较和执行 GC-MS 数据的分子网络分析。MSHub/GNPS 通过无监督非负矩阵分解对化合物的碎片模式进行自动解卷积，并量化碎片模式在样本间的重现性。
