Circular RNAs (circRNAs) play a crucial role in generegulation and association with diseases because of their uniqueclosed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarifytheir functions, a large number of computational approaches foridentifying circRNA formation have been proposed. However, thesemethods fail to fully utilize the important characteristics of backsplicing events, i.e., the positional information of the splice sitesand the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approachcalled SIDE for predicting circRNA back-splicing events using onlyraw RNA sequences. Technically, SIDE employs a dual encoderto capture global and interactive features of the RNA sequence,and then a decoder designed by the contrastive learning to fuseout discriminative features improving the prediction of circRNAsformation. Empirical results on three real-world datasets showthe effectiveness of SIDE. Further analysis also reveals that theeffectiveness of SIDE
