Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.

Journal: PloS one
PMID: 40273141

Abstract

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches.

Authors

  • Yali Guo
    Department of Respiratory Medicine, Beijing Hospital of Traditional Chinese Medicine, Affiliated to Capital Medical University, Beijing, China.
  • Qian Jin
    Department of Respiratory Medicine, Beijing Hospital of Traditional Chinese Medicine, Affiliated to Capital Medical University, Beijing, China.
  • Yi Kang
    Beijing University of Chinese Medicine, Beijing, China.
  • Wenwen Jin
    Medical Engineering Technology and Data Mining Institute, Zhengzhou University, Zhengzhou, 450001, Henan, China.
  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Qian Chen
    Department of Pain Medicine Guizhou Provincial Orthopedics Hospital Guiyang Guizhou China.
  • Jian Liu
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Yu Guang Wang
    School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia. Electronic address: yuguang.wang@unsw.edu.au.

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