Premium
Localized network centrality and essentiality in the yeast–protein interaction network
Author(s) -
Park Keunwan,
Kim Dongsup
Publication year - 2009
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200900357
Subject(s) - centrality , network theory , network science , network analysis , katz centrality , representation (politics) , computational biology , computer science , interaction network , biology , complex network , betweenness centrality , gene , genetics , mathematics , combinatorics , physics , quantum mechanics , politics , world wide web , political science , law
It has been suggested that a close relationship exists between gene essentiality and network centrality in protein–protein interaction networks. However, recent studies have reported somewhat conflicting results on this relationship. In this study, we investigated whether essential proteins could be inferred from network centrality alone. In addition, we determined which centrality measures describe the essentiality well. For this analysis, we devised new local centrality measures based on several well‐known centrality measures to more precisely describe the connection between network topology and essentiality. We examined two recent yeast protein–protein interaction networks using 40 different centrality measures. We discovered a close relationship between the path‐based localized information centrality and gene essentiality, which suggested underlying topological features that represent essentiality. We propose that two important features of the localized information centrality (proper representation of environmental complexity and the consideration of local sub‐networks) are the key factors that reveal essentiality. In addition, a random forest classifier showed reasonable performance at classifying essential proteins. Finally, the results of clustering analysis using centrality measures indicate that some network clusters are closely related with both particular biological processes and essentiality, suggesting that functionally related proteins tend to share similar network properties.