#Network analyzer cytoscape update
#Network analyzer cytoscape software

The Methods section details how CompositeView was constructed and how it is used, including a stress testing analysis for CompositeViewвЂ™s data handling, processing, and efficiency. The remainder of the Introduction section provides the necessary background, motivation, and design criteria for CompositeView. By contrast, built-in interactive composite scoring is not currently available in other generalist visualization tools or even specialist network visualization tools. Interactive composite scoring also provides a pivotal quantitative metric to further optimize user filtering and visualization. Interactive composite scoring and corresponding visualization greatly decreases information overload in complex network or non-network data, enabling effective and efficient actionable insight. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score.

#Network analyzer cytoscape update

The key gap filled by CompositeView is the ability to automatically calculate and update composite scores as the user simultaneously interacts with the data. CompositeView was originally inspired by link prediction and relevance scoring methods, but was generalized for a greater breadth of data.

Here we develop CompositeView, an open-source Python-based data visualization tool, to assist domain specialists in deriving actionable insights from large, complex data sets that can be visualized in network form. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized.

#Network analyzer cytoscape software

Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery Human Development Index (HDI) data and the Framingham cardiovascular study.

CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread).

Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning.