HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction.

ABSTRACT:

Background

Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings.

Results

In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating

SUBMITTER: Shang Y 

PROVIDER: S-EPMC12004644 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature