PROJECT TITLE:

A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks - 2015

ABSTRACT:

Essential proteins are indispensable for cellular life. It is of great significance to spot essential proteins that may help us understand the minimal requirements for cellular life and is additionally very important for drug style. However, identification of essential proteins based mostly on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology within the post-genomic era, a lot of and a lot of protein-protein interaction information can be obtained, that create it potential to review essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology primarily based essential protein discovery methods were to use network centralities. In this paper, we tend to investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the primary time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic plan is that each protein within the network will be viewed as a material particle that creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the price of each protein's topology potential, we have a tendency to can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based mostly strategies TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), data centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.


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