Enhanced Hierarchical Attention Network-Based Drug-Gene Association for Angiotensin Receptors in Periodontal Inflammation

Periodontal inflammation, a chronic condition affecting teeth and supporting structures, is linked to cardiovascular disease. The renin-angiotensin system plays a crucial role in inflammation and oxidative stress, with AT1 and AT2 receptors affecting vascular functions and inflammatory responses. This study aims to utilize angiotensin receptors in periodontal inflammation through enhanced hysterical attention network-based drug-gene association. Data preprocessing is crucial for ensuring the quality and reliability of drug and gene data, particularly in research involving angiotensin receptors, by identifying duplicates and correcting inconsistent formats. Cytoscape was utilized to import drugs and genes linked to angiotensin receptors, thereby constructing and analyzing a network. The Hierarchical Attention Network is an innovative framework for processing structured data with hierarchical relationships. It is suitable for tasks where features can be organized into multi-level structures. The network, with 1,172 nodes and 4,315 edges, has efficient communication, low density, significant connectivity variance, moderate centralization, and four connected components, with a 1.531-second analysis time. The model's R² score is 0.3631, indicating that the features can explain 36.31% of the target variable's variance. However, the model's predictions are about 0.8013 units away from actual values, suggesting room for improvement. Integrating hierarchical attention networks in deep learning models is promising for predicting drug and gene interactions in angiotensin receptors in periodontal inflammation.