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

被引:1
作者
Shang, Yifan [1 ]
Wang, Zixu [2 ]
Chen, Yangyang [1 ]
Yang, Xinyu [1 ]
Ren, Zhonghao [1 ]
Zeng, Xiangxiang [1 ]
Xu, Lei [3 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Univ Tsukuba, Dept Comp Sci, Tsukuba 3058577, Japan
[3] Shenzhen Polytech Univ, Sch Elect & Commun Engn, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Drug-disease association prediction; Drug repositioning; Heterogeneous Network; Contrastive learning; Transformer; PHASE-II TRIAL; PROSTATE-CANCER; COMBINATION THERAPY; ABIRATERONE ACETATE; PREDNISONE; DESMOPRESSIN; DOCETAXEL; ETOPOSIDE; GROWTH; PACLITAXEL;
D O I
10.1186/s12915-025-02206-x
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
BackgroundDrug-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 often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings.ResultsIn this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating multi-omics information. HNF-DDA also proposes the concept of subgraph contrastive learning to capture the local structure of drug-disease subgraphs, learning the high-order semantic information of nodes. Experimental results on two benchmark datasets demonstrate that HNF-DDA outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability to generalize to novel drug and disease categories. Case studies for breast cancer and prostate cancer reveal that 9 out of the top 10 predicted candidate drugs for breast cancer and 8 out of the top 10 for prostate cancer have documented therapeutic effects.ConclusionsHNF-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.
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页数:16
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