A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

Journal: Biological psychiatry
Published Date: Dec 22, 2023

Abstract

BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.

Authors

  • Simeng Ma
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Xinhui Xie
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Zipeng Deng
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Dan Xiang
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Lihua Yao
    Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Lijun Kang
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Shuxian Xu
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Huiling Wang
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China.
  • Gaohua Wang
    Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
  • Jun Yang
    Cardiovascular Endocrinology Laboratory, Hudson Institute of Medical Research, Clayton, Victoria, Australia; Department of Medicine, Monash University, Clayton, Victoria, Australia.
  • Zhongchun Liu
    Department of PsychiatryRenmin Hospital of Wuhan University Wuhan 430074 China.

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