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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation.

Jennifer Gaskell1, Nazareno Campioni1, Juan M Morales2,3

  • 1School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK.

Journal of the Royal Society, Interface
|January 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for analyzing complex systems by automatically learning summary statistics using graph neural networks. This approach simplifies the inference process for simulation models, particularly in collective behavior research.

Keywords:
Bayesian inferenceGaussian processescollective movementemergent propertiesmachine learning

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Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Complex Systems

Background:

  • Inferring processes in collective behavior is challenging.
  • Simulation models capture phenomena but are hard to fit to data.
  • Approximate Bayesian computation (ABC) is useful when likelihood is unavailable.

Purpose of the Study:

  • To develop a method for automatic summary statistics learning in ABC.
  • To bypass the need for manual summary statistics design in complex systems.
  • To improve the tractability of fitting simulation models to data.

Main Methods:

  • Combined Gaussian process accelerated ABC with graph neural networks.
  • Used graph embeddings to encode relational inductive biases.
  • Automatically extracted summary statistics from simulation data.

Main Results:

  • The framework bypasses the need for model-specific summary statistics.
  • Demonstrated effectiveness using a collective animal movement model.
  • Outperformed standard summary statistics and linear regression approaches.

Conclusions:

  • Automatic summary statistics learning via graph neural networks is effective for ABC.
  • This method enhances the analysis of high-dimensional complex systems.
  • Offers a more tractable approach to fitting simulation models to empirical data.