An integration of deep learning with feature fusion for protein-protein interaction prediction

High-throughput biological and large-scale experiments for protein–protein interaction (PPI) identification provides valuable information about PPI networks but are time-consuming and limited in determining PPI between different species. We propose the integration of deep learning with feature fusion for PPI prediction, combining handcrafted features and protein sequence embedding for protein sequence representation. Our proposed method is evaluated on the Yeast full, Yeast core, Human, and eight independent datasets. The experimental results show that our method achieves 95.8% accuracy on the Yeast core, 99.2% accuracy on Human, and 100% accuracy on eight independent datasets. We also perform extensive comparisons with other existing outstanding methods and demonstrate the superior ability of the proposed method.