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Genes that are indispensable for survival are termed essential genes. The analysis and identification of essential genes are very important for understanding the minimal requirements of cellular survival and for practical purposes. Proteins do not exert their function in isolation of one another but rather interact together in PPI networks. A global analysis of protein interaction networks provides an effective way to elucidate the relationships between proteins. With the recent large-scale identifications of essential genes and the production of large amounts of PPIs in humans, we are able to investigate the topological properties and biological properties of essential genes. However, until recently, no one has ever investigated human essential genes using topological and biological properties. In this study, for the first time, 28 topological properties and 22 biological properties were used to investigate the characteristics of essential and non-essential genes in humans. Most of the properties were statistically discriminative between essential and non-essential genes. The F-score was used to estimate the essentiality of each property. The GO-enrichment analysis was performed to investigate the functions of the essential and non-essential genes. Finally, based on the topological features and the biological characteristics, a machine-learning classifier was constructed to predict the essential genes. The results of the jackknife test and 10-fold cross validation test are encouraging, indicating that our classifier is an effective human essential gene discovery method. [ABSTRACT FROM AUTHOR]

Abstract: Proteins do not exert their function in isolation of one another, but interact together in protein–protein interaction (PPI) networks. Analysis of topological properties of proteins in the PPI network is very helpful to understand the function of proteins. However, until recently, no one has ever undertaken to investigate toxin targets by topological properties. In this study, for the first time, 12 topological properties are used to investigate the characteristics of toxin targets in the PPI network. Most of the topological properties are found to be statistically discriminative between toxin targets and other proteins, and toxin targets tend to play more important roles in the PPI network. In addition, based on the topological properties and the sequence information, support vector machine (SVM) is used to predict toxin targets. The results obtained by the jackknife test and 10-fold cross validation are encouraging, indicating that SVM is a useful tool for predicting toxin targets, or at least can play complementary roles in relevant areas. [Copyright &y& Elsevier]