Summary of similarity matrices used in CFGSCDSA.

MotivationThe expression of circular RNAs (circRNAs) has been shown to be strongly correlated with drug sensitivity in human cells. However, experimental validation using wet-lab techniques is costly and inefficient, leaving a substantial portion of circRNA–drug sensitivity associations undiscovered. Therefore, improving the prediction efficiency of circRNA and sensitivity associations remains critical.MethodsHere, we describe a method that integrates collaborative feature learning and graph structure learning to predict associations between circRNAs and drug sensitivity (CFGSCDSA). Specifically, collaborative learning integrated heterogeneous features from diverse data sources, thereby addressing the issue of data sparsity. Furthermore, graph structure learning with a confidence-guided pseudo-labeling strategy was employed to mitigate the detrimental effect of excessive negative samples. Results: Experimental evaluation revealed that CFGSCDSA attained superior performance compared to all competing models. Moreover, case studies provided further evidence of its capability to accurately predict both novel associations and new drug-related links.研究动机：环状RNA（circular RNAs, circRNAs）的表达已被证实与人类细胞的药物敏感性显著相关。然而，采用湿实验技术进行实验验证不仅成本高昂且效率低下，导致大量circRNA-药物敏感性关联尚未被发掘。因此，提升circRNA与药物敏感性关联的预测效率仍具有关键研究价值。 研究方法：本文提出一种整合协同特征学习与图结构学习的方法，用于预测circRNA与药物敏感性之间的关联（命名为CFGSCDSA）。具体而言，协同学习整合了多源异构特征，从而解决了数据稀疏性问题。此外，本研究采用了搭载置信度引导伪标签策略的图结构学习方法，以缓解过多负样本带来的负面影响。 实验结果：实验评估表明，CFGSCDSA的性能优于所有对比模型。此外，案例研究进一步证实了其能够精准预测新型关联及新的药物相关关联。