Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.

The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. However, these methods still exhibit some limitations such as ignoring the effect of noise. In this paper, we proposed a new knowledge graph attribute mining attention network (KAATCDA) to predict circRNA-disease associations based on knowledge graph attribute network (KGA) and attribute mining attention network (AMA). Firstly, KGA is used to learn the feature representation of diseases. Then, the features of circRNAs are obtained using AMA, which are similar to disease feature representations. Finally, the scores of circRNA-disease associations are predicted based on circRNA feature representation and disease feature representation. Experiments of five-fold cross-validation on two datasets demonstrate that KAATCDA outperforms other state-of-the-art methods. In addition, the case study shows our method can effectively predict unknown circRNA-disease associations.