Essential Protein Prediction Based on node2vec and XGBoost

Essential proteins are a vital part of the survival of organisms and cells. Identification of essential proteins lays a solid foundation for understanding protein functions and discovering drug targets. The traditional biological experiments are expensive and time-consuming. Recently, many computational methods have been proposed. However, some noises in the protein-protein interaction (PPI) networks affect the efficiency of essential protein prediction. It is necessary to construct a credible PPI network by using other useful biological information to reduce the effects of these noises. In this article, we proposed a model, Ess-NEXG, to identify essential proteins, which integrates biological information, including orthologous information, subcellular localization information, RNA-Seq information, and PPI network. In our model, first, we constructed a credible weighted PPI network by using different types of biological information. Second, we extracted the topological features of proteins in the constructed weighted PPI network by using the node2vec technique. Last, we used eXtreme Gradient Boosting (XGBoost) to predict essential proteins by using the topological features of proteins. The extensive results show that our model has better performance than other computational methods.