GRAFT: a graph-aware fusion transformer for cancer driver gene prediction.

ABSTRACT: Identifying cancer driver genes is essential for precision oncology, but existing computational methods are often limited by their reliance on single biological networks and their inability to capture long-range molecular dependencies. To address these challenges, we propose GRAFT, a Graph-Aware Fusion Transformer. This framework learns modality-specific features from protein-protein interactions, pathway co-occurrence, and gene semantic similarity using a multi-view graph encoder. These representations are further enriched with two auxiliary feature types: structural encodings derived from network topology and functional embeddings guided by curated gene sets. The integrated features are then processed by a transformer backbone, where a novel edge-attention bias makes the model explicitly

SUBMITTER: Cho SP 

PROVIDER: S-EPMC12790624 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature