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Updated: Jan 11, 2026

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A graph neural network model for inferring interindividual variation from experimental biological data.

Fuminori Kawano1

  • 1Graduate School of Health Science, Matsumoto University, 2095-1 Niimura, Matsumoto City, Nagano, 390-1295, Japan. kawano@t.matsu.ac.jp.

Scientific Reports
|November 12, 2025
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Summary

We developed a graph neural network (GNN) model to uncover hidden biological relationships driving individual differences in responses to stimuli. This computational tool identifies unique molecular pathways in experimental data, advancing personalized medicine research.

Keywords:
Artificial intelligenceDeep learningInterindividual variationMachine learning

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Individual variability in biological responses is well-known.
  • Current computational tools for identifying individual-specific mechanisms are insufficient.
  • Understanding these variations is crucial for personalized medicine.

Purpose of the Study:

  • To introduce a novel graph neural network (GNN) model, the bioreaction-variation network.
  • To infer hidden molecular and physiological relationships underlying interindividual variation in biological responses.
  • To provide a tool applicable at a laboratory scale for analyzing complex biological data.

Main Methods:

  • Developed a five-layer graph neural network (GNN) architecture with multi-head attention and a multi-layer perceptron.
  • Trained the GNN on a domain-specific corpus of ~65,000 studies related to "skeletal muscle".
  • Applied the model to differential gene expression data from mouse skeletal muscle after acute exercise.

Main Results:

  • The GNN successfully inferred individualized networks from experimental data.
  • Identified both common and unique biological pathways across different individuals.
  • Demonstrated the model's ability to extract interpretable, individual-specific biological connectivity patterns.

Conclusions:

  • The bioreaction-variation network provides a proof of concept for customizable, context-based GNN inference.
  • The model effectively addresses biological variation at the individual level.
  • This framework has the potential to advance the understanding of personalized biological responses.