Fig 1.
The overall workflow of CFGSCDSA.
In step A, CFGSCDSA constructs multi-source similarity networks for circRNAs and drugs. In step B, the model employs collaborative feature learning strategy to comprehensively capture information from different data sources. In step C, graph structure learning with confidence-guided pseudo-labeling strategy is adopted to enhance the learning of topological features.
Table 1.
Summary of similarity matrices used in CFGSCDSA.
Fig 2.
The results of PR curves and ROC curves of CFGSCDSA under 5-cv and 10-cv.
Fig 3.
(A) Performance comparison among different methods.
(B) Parameter analysis for edge dropout. (C) Parameter analysis for masking probability. (D) Performance of ablation study.
Table 2.
The top 20 circRNAs associated with drug crizotinib.
Table 3.
The top 20 circRNAs associated with drug etoposide.
Table 4.
The top 10 circRNAs associated with drug belinostat and vorinostat.