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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
A systematic survey of centrality measures for protein-protein interaction networks
BMC Systems Biology, Volume 12, No. 1, Article 80, Year 2018
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Description
Background: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. Results: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. Conclusions: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node. © 2018 The Author(s).
Authors & Co-Authors
Salehzadeh-Yazdi, Ali
Germany, Rostock
Universität Rostock
Hennig, Holger
Germany, Rostock
Universität Rostock
Wolkenhauer, Olaf
Germany, Rostock
Universität Rostock
Mirzaie, Mehdi
Iran, Tehran
Tarbiat Modares University
Jafari, Mohieddin
Iran, Tehran
Pasteur Institute of Iran
Statistics
Citations: 108
Authors: 5
Affiliations: 5
Identifiers
Doi:
10.1186/s12918-018-0598-2
ISSN:
17520509
Study Design
Cross Sectional Study
Study Approach
Quantitative