Identification of essential proteins via the network topology feature and subcellular localisation

The false positive rate and false negative rate in the biological data have a negative impact on prediction of essential proteins by computational methods. In this work, a new method called CNC is developed to detect essential proteins. First, subcellular localisation information is used to evaluate the importance of interactions in the protein networks and the interactions are weighted for the first time. Meanwhile the edge clustering coefficients between the interacting proteins are calculated and serve as the second weighted value. Next, the two weighted technologies are integrated to construct a new weighted protein networks. Finally, each protein in the PPI networks is scored in terms of the weighted interactions between the protein and its direct neighbours. The results show that the new centrality measure CNC is more effective in discovering essential protein compared with other familiar methods.

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