Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA-disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA-disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred's ability to discover potential lncRNA-disease associations.
Keywords: convolutional neural network; deep learning; information flow propagation; lncRNA–disease association; network topological structure.