LAUSR

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms, therefore there are various computable methods proposed to identify essential proteins. Unfortunately, most methods based on network topology consider only the interactions between a protein and its neighboring proteins and not the interactions with its higher-order distance proteins. In this paper, we have proposed the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein-protein interaction (PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. In addition, we also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins by multiple evaluation methods. The results show that DSEP is superior to these 11 methods.