AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.

Abstract

Background: Circular RNAs (circRNAs) are an emerging class of non-coding RNAs with covalently closed loop structures and have been increasingly recognized for their regulatory roles in disease progression and drug response. Accurately identifying circRNA-drug sensitivity associations is therefore essential for understanding therapeutic mechanisms and advancing precision medicine. However, most existing computational methods fail to effectively integrate semantic and structural information and overlook cross-modal feature co-optimization, thereby limiting their predictive performance.

Results: To address these limitations, we develop an end-to-end graph representation learning framework for circRNA-drug sensitivity prediction by jointly modeling homogeneous similarity structures and heterogeneous interaction relationships. The framework integrates fused similarity graphs, semantic feature encoding with pre-norm residual attention, and structural representation learning via graph convolutional networks with Top-K sparse adjacency. In addition, a large-scale heterogeneous graph and a cross-modal collaborative feature mining module are employed to jointly optimize multi-source representations. Experimental results from 5-fold and 10-fold cross-validation, independent test evaluations, ablation study, and case study demonstrate that the proposed framework consistently achieves superior performance compared with state-of-the-art methods.

Conclusions: The proposed framework provides a robust and effective computational strategy for circRNA-drug sensitivity prediction and offers a valuable tool for uncovering potential therapeutic associations, thereby facilitating future research in drug response analysis and precision medicine.