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Ashtiani, M.

Publications and source records attributed to Ashtiani, M..

2 recordsLinked to original sources
bioRxiv2017

CINNA: Deciphering Central Informative Nodes in Network Analysis

In network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us to sieve our large network and distinguish coarse vertices. By that, important decisions could be made based on the circumstances of these vertices on the overall behavior of networks. These vertices are potentially assumed as central or essential nodes. However, the centrality analysis has always been accompanied by a series of ambiguities, since there are a large number of well-known centrality measures, with different algorithms pointing to these essential nodes and there is no well-defined preference. Which measure explore more information in a given network about node essentiality according to the topological features? While here, we tried to provide a pipeline to have a comparison among all proper centrality measures regarding the network structure and choose the most informative one according to dimensional reduction methods. Central Informative Nodes in Network Analysis (CINNA) package is prepared to gather all required function for centrality analysis in the weighted/unweighted and directed/undirected networks.\n\nAvailability and implementationCINNA is available in CRAN, including a tutorial. URL: https://cran.r-proiect.org/web/packages/CINNA/index.html\n\nContact: miafari@pasteur.ac.ir

systems biology↗
bioRxiv2017

Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science

BackgroundNumerous 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.\n\nResultsWe 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 networks 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.\n\nConclusionsThe 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.

systems biology↗