Abstract：
miRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to various human diseases and can be potential biomarkers or therapeutic targets for some diseases, such as cancers. Therefore, accurately predicting miRNA-disease associations is of great importance for understanding and curing diseases. However, how to efficiently utilize the characteristics of miRNAs and diseases and the information on known miRNA-disease associations for prediction is still not fully explored. 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 data. 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 datasets 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.
摘要：
miRNA是许多关键生物过程的重要调节因子。最近的许多研究表明，miRNAs与多种人类疾病密切相关，可以成为某些疾病的潜在生物标志物或治疗靶点。比如癌症。因此,准确预测miRNA与疾病的关联对于理解和治疗疾病具有重要意义。然而,如何有效利用miRNA和疾病的特征以及已知miRNA-疾病关联的信息进行预测仍未得到充分探索。在这项研究中,我们提出了一种预测miRNA-疾病关联的新计算方法。该方法结合了图卷积网络和超图卷积网络。图卷积网络用于从miRNA相似性数据以及疾病相似性数据中提取信息。基于图卷积网络学习的miRNA和疾病的表示，我们进一步使用超图卷积网络来捕获已知miRNA-疾病关联中复杂的高阶相互作用.我们使用不同的数据集和预测任务进行全面的实验。结果表明，该方法始终优于其他几种最新方法。我们还讨论了超参数和模型结构对我们方法性能的影响。一些案例研究还表明，该方法的预测结果可以通过独立实验得到验证。
