Abstract
Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.
Highlights
Essential genes play an indispensable role in supporting the life of organisms, and without them, lethality or infertility is caused
Since we only considered its local properties, the interactions connecting to its K closest neighbors were selected to calculate its interaction frequency entropy (IFE) values (K = 20 according to the recommendation in Reference [35])
All genes in the protein–protein interactive (PPI) network were ranked in descending order according to their ranking scores computed by the FDP, as well as other methods that were compared
Summary
Essential genes (and their encoded proteins) play an indispensable role in supporting the life of organisms, and without them, lethality or infertility is caused. Studying essential genes helps us to understand the basic requirements for cell viability and fertility [1]. Identifying the essential genes of bacteria contributes to finding potential drug targets for new antibiotics [2]. Some researchers pointed out that essential genes have a close relationship with human diseases [3]. Studying essential genes helps us to design novel strategies for disease therapy. The methods to experimentally discover essential genes in biology are time consuming and inefficient. Several recent computational methods have been proposed to identify essential genes [4,5]. These computational methods can be classified into three categories: sequence-based methods, network-based methods, and multi-biological information-based methods
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