Comparative Analysis of Optimization and Hybrid Deep Learning Models for Protein–Protein Interaction Prediction in Bioinformatics

This study compares two recent breakthroughs in bioinformatics optimization: the Integrated Bioinformatics Optimization Model (IBOM) and the Aquila Influenced Shark Smell Optimization (AISSO)-based hybrid deep learning model.While IBOM optimizes human protein interaction predictions using classic optimization techniques such as Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Multi Verse Optimization (MVO), and 5fold Cross Validation (CV), the AISSO-based approach uses hybrid neural networks and innovative optimization approaches to achieve accurate sequence-based PPI prediction.The analysis looks at performance measures, computational efficiency, adaptability to protein datasets, and the practical implications for bioinformatics applications.The results show that IBOM has higher precision and interpretability, whereas AISSO excels in generalization and advanced feature extraction via semantic enrichment.