HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug–disease association prediction

HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug–disease association prediction

Abstract Background Drug–disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods of...

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Bibliographic Details
Main Authors: Yifan Shang, Zixu Wang, Yangyang Chen, Xinyu Yang, Zhonghao Ren, Xiangxiang Zeng, Lei Xu
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Biology
Subjects:
Drug–disease association prediction
Drug repositioning
Heterogeneous Network
Contrastive learning
Transformer
Online Access:https://doi.org/10.1186/s12915-025-02206-x
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https://doi.org/10.1186/s12915-025-02206-x

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