Genetic Algorithm for Optimizing Global Alignment of Protein-Protein Interaction Network

Protein-protein interactions can be modeled as networks. The huge amount of produced Protein-Protein Interaction (PPI)data has many applications in biological studies. These studies improved human knowledge in the life process and diseases. One of these studies is PPI network alignment, which finds the similarity between PPI networks as a biological similarity. Aligning these networks is important to investigate evolutionary pathways or protein complexes. The main challenge of all global PPI network alignment is to improve both accuracy and efficiency. In this research, the accuracy and efficiency of global PPI network alignment are improved by using the genetic algorithm instead of the greedy algorithm and applied in HubAlign framework. The performance of the proposed method is compared with HubAlign based on the total execution time for the alignment process and the correctness of the results of the aligned networks. The new approach enhanced the overall accuracy and reduce execution time compared to the HubAlign approach.