TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.

ABSTRACT: The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods characterize miRNA-disease associations only from single levels, causing the loss of multi-level association information. In this study, we propose TriFusion, a powerful and interpretable deep learning framework for miRNA-disease association prediction. It develops a tri-channel architecture to encode the association features of miRNAs and diseases from different levels and designs a feature fusion encoder to smoothly fuse these features. After training and testing, TriFusion outperforms other leading methods and offers strong interpretability thro

SUBMITTER: Long S 

PROVIDER: S-EPMC11364641 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature