CMAGN: circRNA–miRNA association prediction based on graph attention auto-encoder and network consistency projection
Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses.
Authors
- Yu-Dong Cai
- Lei Chen (ORCID: https://orcid.org/0000-0003-3068-1583)
- Bo Zhou (ORCID: https://orcid.org/0000-0001-9507-2442)
- Alexandre Yin
Institutions
- Shanghai University (CN)
- Shanghai University of Medicine and Health Sciences (CN)
- Shanghai Maritime University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1186/s12859-024-05959-4
- Citations
- 4
- Primary Topic
- Circular RNAs in diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.94