CHNSCDA: circRNA-disease association prediction based on strongly correlated heterogeneous neighbor sampling

Abstract

Circular RNAs (circRNAs) are a special class of endogenous non-coding RNA molecules with a closed circular structure. Numerous studies have demonstrated that exploring the association between circRNAs and diseases is beneficial in revealing the pathogenesis of diseases. However, traditional biological experimental methods are time-consuming. Although some methods have explored the circRNA associated with diseases from different perspectives, how to effectively integrate the multi-perspective data of circRNAs has not been well studied, and the feature aggregation between heterogeneous nodes has not been fully considered. Based on these considerations, a novel computational framework, called CHNSCDA, is proposed to efficiently forecast unknown circRNA-disease associations(CDAs). Specifically, we calculate the sequence similarity and functional similarity for circRNAs, as well as the semantic similarity for diseases. Then the similarities of circRNAs and diseases are combined with Gaussian interaction profile kernels (GIPs) similarity, respectively. These similarities are fused by taking the maximum values. Moreover, circRNA-circRNA associations and disease-disease associations with strong correlations are selectively combined to construct a heterogeneous network. Subsequently, we predict the potential CDAs based on the multi-head dynamic attention mechanism and multi-layer convolutional neural network. The experimental results show that CHNSCDA outperforms the other four state-of-the-art methods and achieves an area under the ROC curve of 0.9803 in 5-fold cross validation (5-fold CV). In addition, extensive ablation comparison experiments were conducted to confirm the validity of different similarity feature aggregation methods, feature aggregation methods, and dynamic attention. Case studies further demonstrate the outstanding performance of CHNSCDA in predicting potential CDAs.