PROJECT TITLE :

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

ABSTRACT:

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins which will help us understand the minimal requirements for cellular life and is also very vital for drug design. However, identification of essential proteins primarily based on experimental approaches are typically time-consuming and expensive. With the event of high-throughput technology within the post-genomic era, more and a lot of protein-protein interaction data will be obtained, which make it attainable to study essential proteins from the network level. There are a series of computational approaches proposed for predicting essential proteins primarily based on network topologies. Most of these topology based mostly essential protein discovery methods were to use network centralities. During this paper, we investigate the essential proteins' topological characters from a fully new perspective. To our knowledge it's the primary time that topology potential is used to spot essential proteins from a protein-protein interaction (PPI) network. The fundamental idea is that every protein in the network will be viewed as a fabric particle which creates a possible field around itself and therefore the interaction of all proteins forms a topological field over the network. By defining and computing the worth of every protein's topology potential, we tend to will obtain a a lot of precise ranking that reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based mostly methods 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. Likewise, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.


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