iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions
The above experimental results indicate that iHofman can effectively predict potential CMIs and has achieved outstanding performance compared with existing methods. It provides a reliable supplementary approach for subsequent biological wet experiments.
Authors
- Chen Jiang (ORCID: https://orcid.org/0000-0002-2831-4365)
- Chang-Qing Yu (ORCID: https://orcid.org/0009-0007-6655-1744)
- Mengmeng Wei (ORCID: https://orcid.org/0009-0009-9453-7653)
- Tai-Long Shi (ORCID: https://orcid.org/0000-0002-0751-3410)
- Xinfei Wang
- Zhu-Hong You
- Lei Wang (ORCID: https://orcid.org/0000-0003-2383-9362)
- Shuai Liang (ORCID: https://orcid.org/0000-0002-9813-6744)
Institutions
- Jilin University (CN)
- China University of Mining and Technology (CN)
- Jilin Province Science and Technology Department (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1186/s12915-025-02260-5
- Citations
- 10
- Primary Topic
- Circular RNAs in diseases
- Type
- article
- Field-Weighted Citation Impact
- 8.06
Funders
- National Natural Science Foundation of China