PPDAMEGCN: Predicting piRNA-Disease Associations Based on Multi-Edge Type Graph Convolutional Network

Recently, many studies have proven that Piwi-interacting RNAs (piRNAs) play key roles in various biological processes and also associate with human complicated diseases. Therefore, in order to accelerate the traditional biomedical experimental methods for determining piRNA-disease associations, many computational approaches have been proposed. However, piRNA-disease associations can be classified into known and unknown associations, each of which may provide distinct types of information. Traditional graph convolutional networks (GCNs) typically treat all edges in a graph as identical, overlooking the fact that different edge types may carry different signals and influence the learning process in unique ways. In this study, we also provide a new piRNA-disease association prediction method, called PPDAMEGCN, based on a multi-edge type graph convolutional network. First, we calculate the piRNA sequence similarity based on the piRNA sequence information and Smith-Waterman method. The disease semantic similarity is also computed by disease ontology (DO). In addition, we calculate the Gaussian interaction profile (GIP) kernel similarities of piRNA and diseases through the known piRNA-disease associations. Then, we construct the piRNA similarity network by integrating the piRNA's sequence similarity and GIP similarity. We also construct the disease similarity network by integrating disease's semantic similarity and GIP similarity. Finally, we obtain the piRNA and disease embeddings by the multi-edge type graph convolutional network model on the heterogenous piRNA-disease association network. The piRNA-disease pair association probability score is calculated by a multilayer perceptron (MLP) with its concatenated embedding. We also compare PPDAMEGCN to other piRNA-disease prediction methods. The experimental results show that our method outperforms compared methods.