Construction of dynamic protein interaction network based on gene expression data and quartile one principle

Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low- or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins.