Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional experimental approaches for validating these associations are resource-intensive and time-consuming. To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA–drug sensitivity associations. Initially, we construct circRNA–circRNA fusion similarity networks and drug–drug fusion similarity networks using similarity network fusion, ensuring a comprehensive integration of information. Based on the above, we built the circRNA homogeneous graph, the drug homogeneous graph, and the circRNA–drug heterogeneous graph. Next, we form the initial node features in the circRNA–drug heterogeneous graph from the homogeneous graph-level perspective and the combined feature-level perspective and complete the prediction of potential associations using the path-masked graph autoencoder in both perspectives. The predictions under both perspectives are finally combined to obtain the final prediction score. Transductive setting experiments and inductive setting experiments all demonstrate that our method, DPMGCDA, outperforms state-of-the-art approaches. Additionally, we verify the necessity of employing dual perspective learning through ablation tests and analyze the effective encoding capability of the path-masked graph autoencoder for features through embedding visualization. Moreover, case studies on four drugs corroborate DPMGCDA’s ability to identify potential circRNAs associated with new drugs.

中文翻译：

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DPMGCDA：通过双视角学习和路径屏蔽图自动编码器破译 circRNA 与药物敏感性的关联


越来越多的证据表明，环状RNA（circRNA）的表达可以影响细胞对药物的敏感性，并显着影响药物疗效。然而，验证这些关联的传统实验方法是资源密集型且耗时的。为了应对这一挑战，我们提出了一种称为 DPMGCDA 的计算框架，利用双视角学习和路径掩蔽图自动编码器来预测 circRNA-药物敏感性关联。首先，我们利用相似网络融合构建了circRNA-circRNA融合相似网络和药物-药物融合相似网络，确保信息的全面整合。在此基础上，我们构建了circRNA同质图、药物同质图和circRNA-药物异质图。接下来，我们从同质图级视角和组合特征级视角形成circRNA-药物异质图中的初始节点特征，并在两个视角下使用路径掩码图自动编码器完成潜在关联的预测。最后将两个视角下的预测相结合，得到最终的预测分数。传导设置实验和感应设置实验都表明我们的方法 DPMGCDA 优于最先进的方法。此外，我们通过消融测试验证了采用双视角学习的必要性，并通过嵌入可视化分析了路径掩码图自动编码器对特征的有效编码能力。此外，对四种药物的案例研究证实了 DPMGCDA 识别与新药相关的潜在 circRNA 的能力。