JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or...

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Bibliographic Details
Main Authors: Jian Guo, Sam Shen, Min Liu, Chenjingyi Wang, Brian Low, Ying Chen, Yaxi Hu, Shipei Xing, Huaxu Yu, Yu Gao, Mingliang Fang, Tao Huan
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Metabolites
Subjects:
untargeted metabolomics
exposomics
feature extraction
data processing
metabolite annotation
Online Access:https://www.mdpi.com/2218-1989/12/3/212

Internet

https://www.mdpi.com/2218-1989/12/3/212

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