Summary: Analyzing biological networks demands scalable annotation tools, yet existing methods fall short in clustering power, statistical flexibility, and broad data compatibility. We introduce Regional Inference of Significant Kinships (RISK), a next-generation tool that overcomes these challenges by integrating community detection algorithms, rigorous overrepresentation analysis, and a modular architecture that supports diverse network types. RISK identifies biologically coherent relationships within networks and generates publication-ready visualizations, as demonstrated by its ability to resolve compact functional modules in Saccharomyces cerevisiae protein-protein interaction and genetic interaction networks. Its application to a high-energy physics citation network reveals structured relationships among research subfields, highlighting its versatility beyond biological systems. As biological and interdisciplinary networks increase in size and complexity, RISK's scalability and adaptability make it a powerful solution for modern network analysis.

Availability and implementation: RISK is compatible with Python 3.8 or later, supports all major operating systems, and can be installed via pip. The software is open source under the GPLv3 license on GitHub (https://github.com/riskportal/risk) and archived on Zenodo (https://doi.org/10.5281/zenodo.17257418). Documentation and a step-by-step Jupyter notebook tutorial are available at https://github.com/riskportal/risk-docs.

• PMID: 41423796
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Journal Article

Authors

Horecka I, Röst H

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