MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network

Background Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. Results In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. Conclusions Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.

Automated summary

The study introduced an efficient framework called MSPCD to infer unknown circRNA-disease associations by integrating biological information and neural network feature extraction, and employing deep neural networks for prediction. Experimental results demonstrated that MSPCD outperformed previous methods, showing promising potential in inferring unknown circRNA-disease associations.