A novel algorithm for identifying essential proteins by integrating subcellular localization

Essential proteins play a crucial role in the survival and development process of life, as they provide all available nutrients to maintain life. Therefore, many researchers pay attention to the identification of essential proteins. As experiments methods are usually costly and time-consuming, more and more computational algorithms have been developed to discover essential proteins based on biological and topological features. Given that the subcellular localization is very important in understanding protein-protein interaction, in this paper, a novel method is proposed to predict essential proteins, which integrates the subcellular compartments information with Pearson correlation coefficient (PCC) of gene expression data. We name this method SCP in this paper. In order to evaluate the prediction performance of our method, several experiments are carried out to compare SCP with other methods. The results demonstrate that SCP has a better prediction performance of essential proteins than other methods.