Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC-MS urinary metabolomics for diseases screening.

Journal: International journal of medical informatics
PMID: 39705916

Abstract

BACKGROUND: Gas chromatography-mass spectrometry (GC-MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC-MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC-MS organic acid profiles.

Authors

  • Haomin Li
    Clinical Data Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052 Hangzhou, Zhejiang, China.
  • Siyuan Gao
    Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.
  • Dan Wu
    Xi'an Aerospace Propulsion Institute, Xi'an 710049, China.
  • Min Zhu
    Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, No.50 Ximeng Road, Taizhou, 317000, China.
  • Zhenzhen Hu
    Department of Genetics and Metabolism, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Binjiang District, Hangzhou City, Zhejiang Province, 310052, China.
  • Kexin Fang
    Department of Genetics and Metabolism, the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China.
  • Xiuru Chen
    Clinical Mass Spectrometry Center, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou 510005, China.
  • Zhou Ni
    Clinical Mass Spectrometry Center, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou 510005, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Beibei Zhao
    Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Xuhui She
    Clinical Mass Spectrometry Center, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou 510005, China; KingMed College of Laboratory Medicine of Guangzhou Medical University, Guangzhou 510005, China. Electronic address: lab-shexuhui@kingmed.com.cn.
  • Xinwen Huang
    Department of Genetics and Metabolism, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, No. 3333 Binsheng Road, Binjiang District, Hangzhou City, Zhejiang Province, 310052, China. 6305022@zju.edu.cn.

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