MASP-CMI: A Sequence-Derived Multi-View Attribute and Similarity-Prior Evidence Learning Framework for circRNA−miRNA Interaction Prediction

Abstract

circRNA−miRNA interactions (CMIs) regulate downstream gene expression through the miRNA sponge mechanism and contribute to the progression of various diseases. Although computational methods have advanced the prediction of potential CMIs, existing approaches still face challenges such as sparse association networks, limited single-sequence representations, and insufficient use of intrinsic RNA sequence evidence. To address these issues, we propose MASP-CMI, a sequence-derived multi-view attribute and similarity-prior evidence learning framework for CMI prediction. Instead of relying on known association networks for node information propagation, MASP-CMI jointly learns multi-view attribute evidence and sequence similarity priors from RNA sequences. The heterogeneous attribute-view evidence learning module adaptively integrates FCGR, CTD, and Doc2Vec features, while the similarity-prior evidence aggregation module enhances RNA representations within homogeneous RNA spaces. Cross-branch alignment and pair-level interaction fusion are further used to predict potential CMIs. Experiments on three benchmark datasets demonstrate that MASP-CMI achieves stable predictive performance, strong robustness, and favorable cold-start generalization. Module ablation experiments and case studies further confirm its effectiveness for potential CMI discovery and experimental prioritization.