Predicting miRNA –Disease Associations by Combining Graph and Hypergraph Convolutional Network

In this study, we propose a novel computational method for predicting miRNA–disease associations. The proposed method combines the graph convolutional network and the hypergraph convolutional network. The graph convolutional network is utilized to extract the information from miRNA-similarity data as well as disease-similarity da ta. Based on the representations of miRNAs and diseases learned by the graph convolutional network, we further use the hypergraph convolutional network to capture the complex high-order interactions in the known miRNA–disease associations. We conduct comprehensive experiments with different datase ts and predictive tasks. The results show that the proposed method consistently outperforms several other state-of-the-art methods. We also discuss the influence of hyper-parameters and model structures on the performance of our method. Some case studies also demonstrate that the predictive results of the method can be verified by independent experiments.Graphical Abstract
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research