HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer's disease.

Journal: Journal of translational medicine
PMID: 39891114

Abstract

BACKGROUND: The traditional process of developing new drugs is time-consuming and often unsuccessful, making drug repurposing an appealing alternative due to its speed and safety. Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. Additionally, these methods often struggle with sparse networks and fail to elucidate the biological mechanisms underlying newly predicted drugs.

Authors

  • Yin-Yuan Su
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan.
  • Hsuan-Cheng Huang
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Yu-Ting Lin
    From the Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Yi-Fang Chuang
    Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
  • Sheh-Yi Sheu
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Chen-Ching Lin
    Institute of Biomedical Informatics, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan. chenching.lin@nycu.edu.tw.

Keywords

External Resources

Popular Topics
Recent Journals