CircGO: Predicting circRNA Functions Through Self-Supervised Learning of Heterogeneous Networks

Abstract

Circular RNAs (circRNAs), a class of non-coding RNAs characterized by their covalently closed loop structures, play active roles in diverse physiological processes through interactions with biological macromolecules. Despite the growing discovery of circRNAs enabled by high- throughput technologies, their functional annotations remain largely unexplored. This highlights the need for automated batch annotation methods to unveil the functional roles of circRNAs. In this study, we present a novel approach for predicting Gene Ontology (GO) functions associated with circRNA by leveraging self-supervised pre-training on circRNA-protein heterogeneous network. First, we construct the heterogeneous network by combining circRNA co-expression data, circRNA-protein association data, and protein-protein interaction (PPI) data. Second, we initialize the features and pseudo-labels for nodes using three graph processing methods including walking, aggregation and clustering. The initialized node features and pseudo labels, combined with protein GO annotations, are employed for heterogeneous graph pre-training. During the pre-training, the node features are learned using a heterogeneous graph attention network and the pseudo-labels are updated using the label propagation algorithm (LPA) with an attention mechanism. Finally, the initial node features are combined with those learned during pre-training to predict circRNA GO terms. Evaluation results on the independent test set reveal the superior performance of our method compared with existing approaches. Furthermore, our analysis underscores the importance of network structure and initialization strategies, highlighting the potential benefits of incorporating additional heterogeneous information and association networks. The source code and dataset are available at https://github.com/Hhhzj-7/CircGO.