A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder

Circular RNAs (circRNAs) play a crucial role in gene
regulation and association with diseases because of their unique
closed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarify
their functions, a large number of computational approaches for
identifying circRNA formation have been proposed. However, these
methods fail to fully utilize the important characteristics of backsplicing events, i.e., the positional information of the splice sites
and the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approach
called SIDE for predicting circRNA back-splicing events using only
raw RNA sequences. Technically, SIDE employs a dual encoder
to capture global and interactive features of the RNA sequence,
and then a decoder designed by the contrastive learning to fuse
out discriminative features improving the prediction of circRNAs
formation. Empirical results on three real-world datasets show
the effectiveness of SIDE. Further analysis also reveals that the
effectiveness of SIDE.