DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions
The DGCLCMI improves circRNA and miRNA feature representation by capturing deep collaborative information, achieving superior performance compared to prior methods. It facilitates the discovery of unknown associations and sheds light on their roles in physiological processes.
Authors
- Quan Zou (ORCID: https://orcid.org/0000-0001-6406-1142)
- Yansu Wang (ORCID: https://orcid.org/0000-0003-1274-4958)
- Chunyu Wang (ORCID: https://orcid.org/0000-0001-5165-7959)
- Chao Cao (ORCID: https://orcid.org/0000-0001-6730-9422)
- Xu Lei (ORCID: https://orcid.org/0000-0003-2271-1287)
- Han Wu (ORCID: https://orcid.org/0000-0002-5599-0625)
- Mengli Li (ORCID: https://orcid.org/0000-0001-5881-2763)
Institutions
- University of Electronic Science and Technology of China (CN)
- Shenzhen Polytechnic University (CN)
- Harbin Institute of Technology (CN)
- Quzhou University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1186/s12915-025-02197-9
- Citations
- 4
- Primary Topic
- Circular RNAs in diseases
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- Shenzhen Polytechnic
- National Natural Science Foundation of China