Novel Protein–Protein Interaction Prediction with Updated Deep Radial Graph Basis Prism Refraction Search Convolutional Networks Model

Predicting Protein–Protein Interactions (PPIs) is essential to comprehending biological functions and is pivotal for drug discovery and disease understanding.However, accurately predicting these interactions remains a difficult issue because of the intricate and multifaceted nature of protein networks. Traditional models often fail to fully capture the intricate relationships between proteins and their interactions, especially when diverse datasets are involved. To address these challenges, a novel approach, named the Deep Radial Graph Basis Prism Refraction Search Convolutional Networks(DRGB-PRSCN) model, is proposed for PPI prediction using three distinct datasets: Human PPI, STRING, and DIP.The proposed method employs Gradient Domain Guided Filtering for effective data preprocessing, ensuring noise reduction while preserving essential features. Feature extraction is carried out using an Elastic Decision Transformer, which effectively captures key protein features. Deep Graph Convolutional Networks (DGCNs) are leveraged to model complex dependencies and interactions among proteins. The DRGB-PRSCN model, with its advanced architecture, is employed to predict the interactions with high precision. The model achieves a performance evaluation score of 99.9%, demonstrating its efficacy in accurately predicting PPI. This approach outperforms traditional methods by providing superior accuracy and robustness, making it highly beneficial for biological network analysis and drug discovery. The DRGB-PRSCN model’s primary benefit is its capacity to efficiently handle diverse datasets and predict PPIs with exceptional precision.