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A systematic survey of centrality measures for protein-protein interaction networks
TLDR
It is concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures) and identify 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. Expand
CINNA: An R/CRAN package to decipher Central Informative Nodes in Network Analysis
TLDR
The 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. Expand
IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis
TLDR
IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from. Expand
Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology
TLDR
This study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting by suggesting that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stroma cells. Expand
Selection of most relevant centrality measures: A systematic survey on protein-protein interaction networks
TLDR
The centrality profile of nodes of yeast protein-protein interaction networks (PPINs) is examined in order to detect which centrality measure is succeeding in predicting influential proteins and it is restated that the determination of important nodes depends on the network topology. Expand
A systematic survey of centrality measures for protein-protein interaction networks
TLDR
The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network and undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Expand
Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science
TLDR
Using PCA and identifying the contribution proportion of the variables, i.e., centrality measures in principal components, is a prerequisite step of network analysis in order to infer any functional consequences, e.g., the essentiality of a node. Expand
CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis
TLDR
The aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods. Expand
Revisiting the Implementation of the Network Centralities: A Survey on Protein-Protein Interaction Networks
TLDR
Unsupervised machine learning methods conduce to undertake a data reduction and thus choosing appropriate variables (centrality measures) as a prerequisite step of network analysis before inferring functional consequences, e.g., the essentiality of a node. Expand