Ess-NEXG: Predict Essential Proteins by Constructing a Weighted Protein Interaction Network Based on Node Embedding and XGBoost

Essential proteins are indispensable in the development of organisms and cells. Identification of essential proteins lays the foundation for the discovery of drug targets and understanding of protein functions. Traditional biological experiments are expensive and time-consuming. Considering the limitations of biological experiments, many computational methods have been proposed to identify essential proteins. However, lots of noises in the protein-protein interaction (PPI) networks hamper the task of essential protein prediction. To reduce the effects of these noises, constructing a reliable PPI network by introducing other useful biological information to improve the performance of the prediction task is necessary. In this paper, we propose a model called Ess-NEXG which integrates RNA-Seq data, subcellular localization information, and orthologous information, for the prediction of essential proteins. In Ess-NEXG, we construct a reliable weighted network by using these data. Then we use the node2vec technique to capture the topological features of proteins in the constructed weighted PPI network. Last, the extracted features of proteins are put into a machine learning classifier to perform the prediction task. The experimental results show that Ess-NEXG outperforms other computational methods.