MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network for

Background: In the treatment of advanced non-small cell lung cancer (NSCLC), the mutation status of the Kirsten rat sarcoma virus oncogene homolog ( Methods: A multi-view adaptive semantics-aware heterogeneous graph framework (MVASA-HGN) based on multimodal medical data was developed to accurately predict Results: The classification results were evaluated on cooperative hospitals and The Cancer Imaging Archive (TCIA) datasets, and ablation experiments and comparison experiments were performed on the components of the framework, while exploring the framework's rationality and interpretability. Accuracy reached 85.29% and specificity reached 89.67% on the test set, indicating that our framework has significant advantages in deeply modeling complex heterogeneous semantics in local structures and fully exploiting and utilizing the rich semantic information preserved in heterogeneous relationships. The source code of MVASA-HGN is available at https://github.com/Yangwanter37/MVASA-HGN. Conclusions: Our proposed MVASA-HGN framework provides a new perspective for multimodal information fusion and creates a new avenue to explore the potential link between images and genes, and the framework provides a non-invasive and cost-effective solution for identifying