AMDECDA: Attention Mechanism Combined With Data Ensemble Strategy for Predicting CircRNA-Disease Association

Abstract

Accumulating evidence from recent research reveals that circRNA is tightly bound to human complex disease and plays an important regulatory role in disease progression. Identifying disease-associated circRNA occupies a key role in the research of disease pathogenesis. In this study, we propose a new model AMDECDA for predicting circRNA-disease association (CDA) by combining attention mechanism and data ensemble strategy. Firstly, we fuse the heterogeneous information including circRNA Gaussian interaction profile (GIP), disease semantics and disease GIP, and then use the attention mechanism of Graph Attention Network (GAT) to focus on the critical information of data, reasonably allocate resources and extract their essential features. Finally, the ensemble deep RVFL network (edRVFL) is utilized to quickly and accurately predict CDA in the non-iterative manner of closed-form solutions. In the five-fold cross-validation experiment on the benchmark data set, AMDECDA achieves an accuracy of 93.10% with a sensitivity of 97.56% in 0.9235 AUC. In comparison with previous models, AMDECDA exhibits highly competitiveness. Furthermore, 26 of the top 30 unknown CDAs of AMDECDA predicted scores are proved by the related literature. These results indicate that AMDECDA can effectively anticipate latent CDA and provide help for further biological wet experiments.