LSNSCDA: Unraveling CircRNA–Drug Sensitivity via Local Smoothing Graph Neural Network and Credible Negative Samples

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This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://github.com/ZiyuFanCSU/LSNSCDA.