DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

Detecting protein complexes from Protein-Protein interaction network (PPI) is the essence of discovering the rules of cellular world. There is a large amount of PPI data available generated from high throughput experimental data. The huge size of data persuades us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems and so the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and reduce the noise of PPI. Also, gene expression data are used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and create dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms.