DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.

Journal: BMC medical informatics and decision making
PMID: 39563302

Abstract

BACKGROUND: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and completeness of clinical data required for training deep learning models remains a significant challenge. This study aims to explore a new method that reduces data dependency and achieves predictive performance comparable to existing research.

Authors

  • Haoyu Tian
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100063, Beijing, China.
  • Xiong He
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100063, Beijing, China.
  • Kuo Yang
  • Xinyu Dai
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100063, Beijing, China.
  • Yiming Liu
  • Fengjin Zhang
    Department of Nephrology, Third Hospital of Hebei Medical University, China Academy of Chinese Medical Sciences, Shijiazhuang, 050051, Hebei, China.
  • Zixin Shu
  • Qiguang Zheng
    School of Computer and Information Technology, Beijing Jiaotong University, China.
  • Shihua Wang
    Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, Beijing, China.
  • Jianan Xia
    Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
  • Tiancai Wen
    Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
  • Baoyan Liu
    China Academy of Chinese Medical Sciences.
  • Jian Yu
    Key laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, 300192, China; Tianjin Key Laboratory for Organ Transplantation, Tianjin First Center Hospital, Tianjin, 300192, China; Department of Liver Transplantation, Tianjin Medical University First Center Clinical College, Tianjin, 300192, China; Tianjin Key Laboratory of Molecular and Treatment of Liver Cancer, Tianjin First Center Hospital, Tianjin, 300192, China.
  • Xuezhong Zhou
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

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