Yuwanati Monal, Thiyagarajan Santhanamari, Ealla Kranti Kiran Reddy, Jain Yash, Yadalam Pradeep Kumar, Mullainathan Senthil Murugan, Nanda Anima, Sahoo Samir, Uti Daniel Ejim
Department of Oral and Maxillofacial Pathology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University, Arar, Kingdom of Saudi Arabia.
PLoS One. 2025 Jul 3;20(7):e0327619. doi: 10.1371/journal.pone.0327619. eCollection 2025.
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.
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.
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.
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.
本研究评估图注意力网络（GAT）和图采样与聚合（GraphSAGE）在预测口腔鳞状细胞癌中糖皮质激素的药物-基因相互作用方面的有效性，从而有助于制定更好的治疗策略。
我们使用了一个经过整理的数据集，其中包含已知的药物-基因相互作用和相应的分子谱。实施GAT和GraphSAGE来模拟药物-基因关系的生物网络。使用准确率、精确率、召回率和F1分数指标进行实验，以评估每个模型的性能。
网络分析详细显示了一个具有稀疏结构、中等连通性和低聚类性的包含174个节点和409条边的网络，表明节点连接具有多样性。分析证实了一个具有高效计算时间的完全连通网络。在比较模型时，GraphSAGE的表现优于GAT，具有更高的准确率（0.949对0.947）、更好的宏平均F1分数（0.275对0.195）和更高的AUC-ROC（0.780对0.514），表明其具有更强的类别区分能力。两个模型都达到了较高的准确率，但GraphSAGE在F1和AUC-ROC方面的优异分数表明其在精确率和召回率方面实现了更有效的平衡。结果表明，GAT和GraphSAGE都能有效地预测药物-基因关联。然而，在识别口腔鳞状细胞癌背景下相关的糖皮质激素相互作用方面，GAT的表现优于GraphSAGE，实现了更高的准确率和F1分数。
我们的研究结果突出了先进的基于图的方法在阐明口腔鳞状细胞癌中药物相互作用方面的功效。特别是GAT，在准确预测药物-基因关联方面显示出前景，这可能有助于个性化治疗方法的发展。未来的研究将集中在改进这些模型，并探索其他药物化合物，以了解它们在口腔鳞状细胞癌治疗中的适用性。
