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Related Experiment Video

Updated: Jul 12, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Identification of disease modules using higher-order network structure.

Pramesh Singh^1,2, Hannah Kuder³, Anna Ritz¹

¹Biology Department, Reed College, Portland, OR 97202, United States.

Bioinformatics Advances

|October 20, 2023

Summary

This summary is machine-generated.

This study introduces a novel higher-order community detection method for biological networks. This approach uncovers disease-associated modules missed by traditional clustering, improving disease gene association predictions.

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Published on: September 25, 2021

Area of Science:

Systems biology
Network science
Computational biology

Background:

Protein interaction networks are crucial for understanding molecular processes and diseases.
Existing clustering methods primarily focus on edge density, neglecting higher-order interactions.
Disease genes exhibit complex higher-order structures in these networks.

Purpose of the Study:

To develop and evaluate a higher-order community detection method for biological networks.
To identify novel disease-associated modules beyond the capabilities of edge-based clustering.
To enhance the prediction of disease-gene associations.

Main Methods:

Proposed a novel higher-order community detection algorithm.
Applied the method to four distinct protein-protein interaction networks.
Utilized genome-wide association study data to identify disease modules.

Main Results:

Identified biologically significant and disease-associated modules missed by conventional clustering.
Discovered new disease modules from genome-wide association study data.
Outperformed top methods in a Disease Module DREAM Challenge for novel module discovery.

Conclusions:

Higher-order community detection provides a more comprehensive network analysis.
This approach reveals hidden community structures and disease modules.
The method enhances the prediction of novel disease-gene associations.