ArticleF1000Research2014
Biological network analysis with CentiScaPe: centralities and experimental dataset integration.
Article in F1000Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.
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Who cites it
104 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Article
44 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.