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: Guo, Jian, Shen, Sam, Liu, Min, Wang, Chenjingyi, Low, Brian, Chen, Ying, Hu, Yaxi, Xing, Shipei, Yu, Huaxu, Gao, Yu, Fang, Mingliang, Huan, Tao
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Engineering::Environmental engineering
Untargeted Metabolomics
Exposomics
Online Access:https://hdl.handle.net/10356/164852
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Institution: Nanyang Technological University
Language: English

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https://hdl.handle.net/10356/164852

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