A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

Journal: Clinica chimica acta; international journal of clinical chemistry
PMID: 38621587

Abstract

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD).

Authors

  • Liyuan Han
    State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Xi Chen
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Ruijie Zhang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Tian Zhao
    Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China. zhao_tian3300@163.com.
  • Liyuan Pu
    Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No 2 Hospital, Ningbo 315000, China; Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China.
  • Yi Huang
    Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Hongpeng Sun
    School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu province, China. Electronic address: sunhongpeng_2023@126.com.

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