NeKo – Network Konstructor¶
NeKo is a Python package for extracting, visualising, converting, and studying interactions from databases into executable activity flow-based models. It is built on top of OmniPath, PyPath, and Atopo.
Key features¶
| Feature | Description |
|---|---|
| Network creation | Build signalling networks from curated prior-knowledge databases |
| Node connection | Connect nodes and subnetworks with flexible stepwise strategies |
| Gene-to-phenotype | Map gene sets to phenotypic categories via Gene Ontology |
| Visualisation | Render networks with Graphviz or the interactive yFiles widget |
| Export | Export to SIF, GML, GraphML, BND/CFG logical model formats |
| Network history | Automatic snapshots, branching state management, and HTML/SVG rendering |
| Comparison | Compare two networks and highlight differences |
Citation¶
If you use NeKo in your research, please cite:
Ruscone M, Tsirvouli E, Checcoli A, Turei D, Barillot E, et al. (2025) NeKo: A tool for automatic network construction from prior knowledge. PLOS Computational Biology 21(9): e1013300. https://doi.org/10.1371/journal.pcbi.1013300
Quick start¶
from neko.core.network import Network
from neko.inputs import Universe
from neko._visual.visualize_network import NetworkVisualizer
# Load the interaction universe from OmniPath
resources = Universe()
resources.build()
# Create a network from a list of genes
net = Network(["EGFR", "KRAS", "TP53", "AKT1"], resources=resources.interactions)
# Connect nodes using the available database interactions
net.connect_nodes()
# Visualise
vis = NetworkVisualizer(net)
vis.render()
See the Tutorials for full worked examples, or jump to the API Reference for detailed documentation.
Comparison with Sphinx docs¶
During the transition period both documentation backends are live. Feel free to use whichever you prefer and let us know via a GitHub issue:
- This site (MkDocs / Material) – https://sysbio-curie.github.io/Neko/
- Sphinx / RTD – https://sysbio-curie.github.io/Neko/sphinx/