Predicting circRNA-disease associations using meta path-based representation learning on heterogenous network

Abstract

Circular RNA (circRNA) is a new class of regulatory non-coding RNAs modulating gene expression by acting as a microRNA (miRNA) sponge, RNA binding protein sponge and translational regulator. A increasing number of experimental studies have shown that circRNA plays an important role in the development of diseases, and circRNA biomarkers are helpful for the diagnosis and treatment of various human diseases. There is a pressing demand to establish an effective computational method to identify the associations between circRNAs and diseases. In this paper, we propose a new computational framework for the prediction of the circRNA-disease associations. In particular, we calculated meta path-based feature vectors for each circRNAdisease pair on a heterogeneous information network (HIN) that integrated multiple subnetworks, including circRNA similarity network, disease similarity network, protein similarity network, circRNA-disease associations, circRNA-protein associations and protein-disease associations. A positive-unlabeled learning algorithm was adopted to generate negative samples, and a random forest classifier was trained to predicted circRNA-disease associations. We conducted performance comparison with three popular methods on the CircR2Disease dataset. The experimental results show that our method outperform other existing methods by achieving AUC 0.983 on 5-fold cross-validation.