RT Journal Article
SR Electronic
T1 A systematic survey of centrality measures for protein-protein interaction networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 149492
DO 10.1101/149492
A1 Ashtiani, Minoo
A1 Salehzadeh-Yazdi, Ali
A1 Razaghi-Moghadam, Zahra
A1 Hennig, Holger
A1 Wolkenhauer, Olaf
A1 Mirzaie, Mehdi
A1 Jafari, Mohieddin
YR 2017
UL http://biorxiv.org/content/early/2017/10/09/149492.abstract
AB 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.