Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms.

ABSTRACT:

Motivation

Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance.

Results

This study presents a method that combines multi-view shared units and multichannel attent

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11919450 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature