Prediction of rescuable cancer mutants in p53 mutation network using centrality measures
by R. Geetha Ramani; P. Nancy; Shomona Gracia Jacob
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 25, No. 2/3/4, 2017

Abstract: In graph theory and network analysis, gauges of centrality discover significant vertices within a graph or network. Centrality which is optimal for one application is often sub-optimal for a different application. The traditional centrality measures, degree focuses on visibility, betweenness focuses on vertex control of communication, and closeness focuses on vertex independency. This research aims at proposing a novel centrality measure - Entropy based Shortest Path (ESP) which focuses on a node's potential to expand interaction and identify rescuable cancer mutants in the p53 mutation records that depict the amino-acid substitutions obtained by yeast assays. We show that the traditional centrality measures resulted in different assessment of the vertices (proteins) while the proposed measure tailored to the investigations of p53 mutation network was instrumental in the identification of new rescuable cancer mutants. The proposed measure is also applied to social networks like Zachary karate network to validate its correctness in identifying influential nodes.

Online publication date: Tue, 31-Oct-2017

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