skills.rest
stellaromics
stellaromicsOFFICIAL
Explore all skills by this author →
GitHub

bio-data-visualization-network-visualization

Visualize biological networks as static plots, interactive PyVis networks, and Cytoscape exports.

Updated Aug 23, 2026
Search Tags:#interactive#bioinformatics#network#networkx#visualize#cytoscape#pyvis
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-data-visualization-network-visualization-stellaromics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-data-visualization-network-visualization
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-data-visualization-network-visualization
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-data-visualization-network-visualization-stellaromics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Visualize biological networks from diverse data sources to produce clear, publication-ready figures and interactive visualizations for exploration and communication.

Core Features & Use Cases

  • Visualize protein–protein interaction networks, gene regulatory networks, and co-expression modules using NetworkX, PyVis, and Cytoscape automation.
  • Generate static plots, interactive HTML networks, and exportable publication-quality figures for papers, posters, and reports.
  • Use Case: A researcher compares network layouts across datasets and shares interactive networks with collaborators.

Quick Start

Visualize a network from your bio-data and render a static figure or interactive HTML network with Cytoscape/PyVis.

Frequently Asked Questions about bio-data-visualization-network-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I visualize a protein-protein interaction network for publication?▼

This Skill visualizes biological networks by processing your data through NetworkX to produce static plots, interactive PyVis HTML networks, and Cytoscape exports suitable for publication-ready figures.

Can I export my gene regulatory network to Cytoscape for further analysis?▼

Yes, you can export gene regulatory networks to Cytoscape. The Skill uses Cytoscape automation support to render visuals and export figures for collaboration and publication.

What is the best way to create interactive HTML networks from co-expression modules?▼

The best way to create interactive HTML networks from co-expression modules is using PyVis, which renders your biological data into an interactive format for exploration and sharing with collaborators.

Do I need Python and NetworkX to render biological network visuals?▼

Yes, you need Python tooling including NetworkX, PyVis, and Cytoscape automation support to render visuals, generate interactive networks, and export publication-quality figures.

Does PyVis work with NetworkX for visualizing bioinformatics networks?▼

PyVis works with NetworkX for visualizing bioinformatics networks, enabling you to transition from static network layouts to interactive HTML visualizations for exploration and communication.

When should I use static plots instead of interactive PyVis networks for biological data?▼

Use static plots for publication-ready figures in papers and posters where fixed layouts are needed. Use interactive PyVis networks when you need to share explorable network data with collaborators.

Related Skills

Explore More Data & Analytics Skills→
nahisahonahisaho
3

scientific-network-analysis

Construct and analyze biological networks with NetworkX to reveal hubs and modular structure.

Community
Advanced
ya-wayya-way

bio-data-visualization-network-visualization

Visualize biological networks as static figures and interactive HTML using NetworkX and PyVis.

Community
Intermediate
tassiovaletassiovale
13

scikit-bio

Manipulate biological sequences, alignments, trees, and diversity metrics in Python.

Community
Advanced
robotlearning123robotlearning123

torchdrug

Automate molecular property prediction, protein modeling, and retrosynthesis with PyTorch GNNs.

Community
Advanced