JPA: joint metabolic feature extraction increases the depth of chemical coverage for LC-MS-based metabolomics and exposomics

• 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
• Institution: Nanyang Technological University

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...