HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug–disease association prediction
HNF-DDA incorporates all-pairs message passing and subgraph capture strategies into heterogeneous network embedding, enabling effective learning of drug and disease representations enriched with heterogeneous information, while also demonstrating significant potential for applications in drug repositioning.
Authors
- Xiangzheng Fu (ORCID: https://orcid.org/0000-0001-6840-2573)
- Yifan Shang
- Zhonghao Ren
- Lei Xu (ORCID: https://orcid.org/0000-0002-6440-6881)
- Zixu Wang (ORCID: https://orcid.org/0009-0005-2901-5753)
- Yangyang Chen (ORCID: https://orcid.org/0000-0002-3521-475X)
- Xinyu Yang (ORCID: https://orcid.org/0009-0004-4716-8641)
Institutions
- University of Tsukuba (JP)
- Hunan University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1186/s12915-025-02206-x
- Citations
- 3
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 4.51
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hunan Province
- Department of Education of Guangdong Province