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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.

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|November 25, 2025
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This summary is machine-generated.

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.

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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.