Biological network analysis with CentiScaPe: centralities and experimental dataset integration
نویسندگان
چکیده
منابع مشابه
Biological network analysis with CentiScaPe: centralities and experimental dataset integration
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 comprehensiv...
متن کاملBiological network analysis with CentiScaPe: centralities
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 comprehensiv...
متن کاملAnalyzing biological network parameters with CentiScaPe
SUMMARY The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The...
متن کاملVisual analysis of network centralities
Centrality analysis determines the importance of vertices in a network based on their connectivity within the network structure. It is a widely used technique to analyse network-structured data. A particularly important task is the comparison of different centrality measures within one network. We present three methods for the exploration and comparison of centrality measures within a network: ...
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ژورنال
عنوان ژورنال: F1000Research
سال: 2014
ISSN: 2046-1402
DOI: 10.12688/f1000research.4477.1