Graph convolution network based on meta-paths and mutual information for drug-target interaction prediction
The experimental results illustrate that GCNMM exhibits superior performance to existing baseline models in DTI prediction. Furthermore, case studies validate the practical effectiveness of GCNMM, highlighting its potential in DTI prediction and drug repositioning.
Authors
- Shujuan Cao (ORCID: https://orcid.org/0009-0002-4085-3123)
- Tiantian Chang
- Zhu Qiu (ORCID: https://orcid.org/0009-0009-2937-8799)
- Qiqige Wuyun (ORCID: https://orcid.org/0000-0002-7228-903X)
- Fang‐Xiang Wu (ORCID: https://orcid.org/0000-0002-4593-9332)
- Binying Cai
Institutions
- Tiangong University (CN)
- University of Saskatchewan (CA)
- Xi’an University (CN)
- Xi’an University of Posts and Telecommunications (CN)
- Michigan State University (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1186/s12859-025-06295-x
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Tianjin City