Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 1, 2025

Abstract

BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, while computational methods have been shown to significantly enhance research efficiency. However, existing methods for predicting metabolite-disease associations primarily depend on predefined similarity metrics and static network structures, often failing to capture the complex interactions among node neighborhoods within metabolite and disease networks. This limitation hinders the capture of deeper dynamic relationships between metabolites and diseases, resulting in information loss and noise that deteriorate prediction performance.

Authors

  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Shiyu Liu
    State Key Laboratory of Oral Diseases, Sichuan University, Chengdu, China.
  • Xiaoxin Du
    Computer and Control Engineering College, Qiqihar University, Qiqihar, 161006, China.
  • Jianfei Zang
    School of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, PR China; Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar 161006, PR China.
  • Chunyu Zhang
    Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Xue Yang
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Yang He
    Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.

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