Essential Proteins Prediction Method Based on Dynamic Network Segmentation

Essential proteins, as the essential substances in proteins, are not only of great importance in studying the regulation of cell growth, but also lay a theoretical foundation for the further study of diseases. At present, most of the methods for protein identification are static and dynamic network methods based on gene expression information and protein-protein interaction (PPI) network, but these methods do not consider the periodicity of gene expression regulation, and cannot accurately describe the protein networks periodically regulated by genes. Therefore, the concept of periodic gene expression is introduced on the basis of dynamic gene expression, and a dynamic network segmentation method is proposed. In this method, the noise data in the gene expression data is filtered by constructing the gene “active” expression matrix and the expression at each moment is classified into “active” and “inactive” expression states. The periods are divided according to the gene “active” expression matrix to characterize the dynamic changes of gene expression over continuous time periods. The segmented “active” expression matrix is applied to act on the protein-protein interaction network to generate the protein periodic subnetworks. Finally, the importance of the protein nodes in the network is measured by integrating each protein periodic subnetwork. The experimental results show that the method can effectively improve the prediction rate of essential proteins in yeast, E.coli and human bladder data.