2. To explore the potential of deep learning architectures, such as neural networks, for enhancing PPI prediction accuracy. 3. To evaluate the impact of integrating more biological data, such as gene expression data, on the predictive capabilities of these models. 4. To provide insights into the interpretability of model decisions and the visualization of predicted PPIs. 5. To discuss the applications of accurate PPI prediction in biological research and drug discovery.