Detecting functional modules in dynamic protein-protein interaction networks using Markov Clustering and Firefly Algorithm

Markov Clustering (MCL) is a popular algorithm for clustering networks in bioinformatics such as Protein-Protein Interaction (PPI) networks and especially, shows excellent performance in clustering Dynamic Proteinprotein Interaction Networks (DPIN). However, a limitation of MCL and its variants (e.g. regularized MCL and soft regularized MCL) is that the clustering results are mostly dependent on the parameters that user-specified. However we know that different networks with various scales need different parameters. In this article, we propose a new MCL method based on the Firefly Algorithm (FA) to optimize its parameters. The results on DIP dataset show that the new algorithm outperforms the state-of-the-art approaches in terms of accuracy of identifying functional modules on a real DPIN.