The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.
Effect of Community Structures in Protein–Protein Interaction Network in Cancer Protein Identification
