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Mining higher-order triadic interactions
Marta Niedostatek1,2, Anthony Baptista1,2,3, Jun Yamamoto4
1School of Mathematical Sciences, Queen Mary University of London, London, UK.
Nature Communications
|November 25, 2025
Summary
This study introduces the Triadic Perceptron Model (TPM) to analyze higher-order interactions in complex systems. It reveals how triadic interactions influence mutual information and develops a method to identify them in biological data.
Area of Science:
- Complex Systems Biology
- Network Science
- Computational Biology
Background:
- Complex systems exhibit higher-order interactions beyond simple pairwise connections.
- Triadic interactions, involving one node regulating two others, are crucial in biological systems but often overlooked.
- Existing models primarily focus on pairwise interactions, neglecting higher-order dynamics.
Purpose of the Study:
- To propose a model for understanding how triadic interactions affect system dynamics.
- To develop an algorithm for extracting triadic interactions from data.
- To identify novel triadic interactions in biological datasets, specifically gene expression data.
Main Methods:
- Development of the Triadic Perceptron Model (TPM) to quantify the effect of triadic interactions on mutual information.
- Formulation of the Triadic Interaction Mining (TRIM) algorithm for extracting triadic interactions from node metadata.
- Application of the TRIM algorithm to gene expression data.
Main Results:
- The Triadic Perceptron Model demonstrates that triadic interactions can modulate mutual information between connected nodes.
- The Triadic Interaction Mining algorithm successfully extracts potential triadic interactions from complex datasets.
- New candidate triadic interactions relevant to Acute Myeloid Leukemia were identified in gene expression data.
Conclusions:
- Triadic interactions play a significant, often ignored, role in the dynamics of complex systems.
- The proposed Triadic Perceptron Model and TRIM algorithm provide a novel framework for studying higher-order interactions.
- This approach can enhance understanding of complex biological systems, with potential applications in ecology and climate science.
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