Graph attention networks for predicting drug-gene association of glucocorticoid in oral squamous cell carcinoma: A comparison with GraphSAGE.

BACKGROUND

The present study evaluates the effectiveness of Graph Attention Networks (GAT) and GraphSAGE in predicting drug-gene interactions for glucocorticoids in oral squamous cell carcinoma, thereby aiding in developing better treatment strategies.

MATERIALS AND METHODS

We utilized a curated dataset containing known drug-gene interactions and corresponding molecular profiles. Both GAT and GraphSAGE were implemented to model the biological networks of drug-gene relationships. Experiments were conducted to evaluate each model's performance using accuracy, precision, recall, and F1-score metrics.

RESULTS

The network analysis details 174 nodes and 409 edges with a sparse structure, moderate connectivity, and low clustering, indicating a diverse node connection. The analysis confirms a fully connected network with efficient computation time. In comparing models, GraphSAGE outperforms GAT with higher accuracy (0.949 vs. 0.947), better macro-averaged F1 score (0.275 vs. 0.195), and higher AUC-ROC (0.780 vs. 0.514), suggesting stronger class-distinction capabilities. Both models achieve high accuracy, but GraphSAGE's superior scores in F1 and AUC-ROC indicate a more effective balance in precision and recall. The results demonstrated that both GAT and GraphSAGE effectively predicted drug-gene associations. However, GAT outperformed GraphSAGE, achieving higher accuracy and F1 scores in identifying relevant glucocorticoid interactions in the context of OSCC.

CONCLUSION

Our findings highlight the efficacy of advanced graph-based methodologies in elucidating drug interactions in OSCC. GAT, in particular, shows promise for accurately predicting drug-gene associations, which may facilitate personalized therapeutic approaches. Future research will focus on enhancing these models and exploring additional drug compounds to understand their applicability in OSCC treatment.