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

Authors: Xiwei Tang; Xuejun Yang; Yongfan Li; Wei Hu; Mingcai Zheng; Wei Peng; Minzhu Xie

Addresses: School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China; College of Computer, National University of Defense Technology, Changsha 410073, China ' College of Computer, National University of Defense Technology, Changsha 410073, China ' School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China ' School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China ' School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China ' Computer Center, Kunming University of Science and Technology, Kunming 650500, China ' College of Physics and Information Science, Hunan Normal University, Changsha 410081, China

Abstract: 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.

Keywords: essential proteins; sub-cellular localisation; protein-protein interaction; PPI; protein identification; network topology; bioinformatics; edge clustering coefficients; protein networks.

DOI: 10.1504/IJDMB.2016.082210

International Journal of Data Mining and Bioinformatics, 2016 Vol.16 No.4, pp.328 - 344

Received: 27 Dec 2016
Accepted: 28 Dec 2016

Published online: 12 Feb 2017 *

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