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A systematic survey of centrality measures for protein-protein interaction networks

Minoo Ashtiani, View ORCID ProfileAli Salehzadeh-Yazdi, View ORCID ProfileZahra Razaghi-Moghadam, View ORCID ProfileHolger Hennig, View ORCID ProfileOlaf Wolkenhauer, View ORCID ProfileMehdi Mirzaie, View ORCID ProfileMohieddin Jafari
doi: https://doi.org/10.1101/149492
Minoo Ashtiani
1Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, P.O. Box 13164, Tehran, Iran
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Ali Salehzadeh-Yazdi
2Department of Systems Biology and Bioinformatics, University of Rostock, Universitaetsplatz 1, 18051 Rostock, Germany
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Zahra Razaghi-Moghadam
3Faculty of New Sciences and Technologies, University of Tehran, P.O. Box 143995-71, Tehran, Iran
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Holger Hennig
2Department of Systems Biology and Bioinformatics, University of Rostock, Universitaetsplatz 1, 18051 Rostock, Germany
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Olaf Wolkenhauer
2Department of Systems Biology and Bioinformatics, University of Rostock, Universitaetsplatz 1, 18051 Rostock, Germany
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Mehdi Mirzaie
4Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, P.O. Box 14115-134, Tehran, Iran
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  • For correspondence: mirzaie@modares.ac.ir m_jafari@pasteur.ac.ir
Mohieddin Jafari
1Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, P.O. Box 13164, Tehran, Iran
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  • For correspondence: mirzaie@modares.ac.ir m_jafari@pasteur.ac.ir
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Abstract

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 09, 2017.
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A systematic survey of centrality measures for protein-protein interaction networks
Minoo Ashtiani, Ali Salehzadeh-Yazdi, Zahra Razaghi-Moghadam, Holger Hennig, Olaf Wolkenhauer, Mehdi Mirzaie, Mohieddin Jafari
bioRxiv 149492; doi: https://doi.org/10.1101/149492
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A systematic survey of centrality measures for protein-protein interaction networks
Minoo Ashtiani, Ali Salehzadeh-Yazdi, Zahra Razaghi-Moghadam, Holger Hennig, Olaf Wolkenhauer, Mehdi Mirzaie, Mohieddin Jafari
bioRxiv 149492; doi: https://doi.org/10.1101/149492

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