A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder
Year: 2024
Type: article
Abstract: 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 back-splicing 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.
Authors: Chengxin He, Lei Duan, Huiru Zheng, Xinye Wang, Lili Guan, Jiaxuan Xu
Institutions: Sichuan University, University of Ulster
Language: English
Topic: Circular RNAs in diseases
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Award: 面向多源时态数据的异化子空间挖掘研究
OpenAlex ID: W4402208601