Investigating the Role of HPC in AI-based Protein-Protein Interaction Analysis

Integrating High-Performance Computing (HPC) with Artificial Intelligence (AI) has greatly expanded the area of protein-protein interaction (PPI) analysis, which is essential for comprehending biological processes. In this paper, we studied the importance of HPC in AI-based PPI analysis, providing a unique technique that integrates deep learning, probabilistic modeling, and efficient network analysis. We evaluated the predicted accuracy and computational efficiency of our new technique with six established methods for PPI analysis. We found that the Area Under the ROC Curve (AUC) for the suggested technique was much greater than that for the baseline method. The suggested method's AUC values routinely outperformed those of the gold standard approaches, demonstrating its superiority in predicting protein interactions. Accurate biological insights and applications in drug development and disease understanding rely on this improved prediction capacity. We also evaluated the approaches' efficacy by computing their running times. The suggested technique was shown to be very effective, using much less time for analysis than conventional approaches. This is an enormous benefit, particularly for time-sensitive or extensive PPI study projects. Our study concludes that HPC is crucial for AI-based PPI analysis. The suggested approach shows impressive performance in terms of predicted accuracy and computational economy since it combines deep learning, probabilistic modeling, and HPC-accelerated network analysis. This study advances bioinformatics and systems biology by developing a robust and time-saving resource for PPI analysis. The suggested technique shows promise for additional progress in drug development, illness detection, and functional genomics, all of which rely heavily on a knowledge of protein interactions.