Predicting Diseaserelated RNA Associations based on Graph Convolutional Attention Network Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are timeconsuming and laborintensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential diseaseRNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other diseaseRNAs association prediction methods, GCAN operates the computation process from global structure of diseaseRNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in diseaseRNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different diseaseRNAs networks: diseasemiRNA and diseaselncRNA. Comparisons of several stateoftheart methods using diseaseRNAs networks show that our novel frameworks outperform baselines by a wide margin in potential diseaseRNAs associations.

Predicting genedisease associations via graph embedding and graph convolutional networks Identifying diseaserelated genes provides essential information for physiology, pathology and pharmacology. With the rapid growth of highthroughput sequencing technology and genomewide association studies, a large number of genedisease associations have been accumulated. These associations, coupled with the existing generelated and diseaserelated databases, enable prediction of unknown associations by computational approaches. In this article, we proposed a new graphbased machine learning framework to predict the disease related genes. We first constructed a heterogeneous genedisease association graph by integrating multiple biomedical knowledge bases, and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the genedisease associations. In our method, we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability. We conducted experiments on DisGeNet dataset in 10fold cross validation. The experimental results suggested that our method significantly outperformed the existing methods according to the performance of gene prioritization. The performance comparison of the variations of our method showed the efficacy of each module in our method.