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Optimal Recursive Expert-Enabled Inference in Regulatory Networks.

Amirhossein Ravari1, Seyede Fatemeh Ghoreishi2, Mahdi Imani1

  • 1Department of Electrical and Computer Engineering, Northeastern University.

IEEE Control Systems Letters
|January 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an expert-enabled method to improve biological network inference by integrating expert knowledge with data. This approach enhances accuracy in understanding complex gene regulatory networks and microbial communities.

Keywords:
Boolean networksGene Regulatory NetworksInferenceInverse Reinforcement Learning

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate inference of biological systems like gene regulatory networks (GRNs) is crucial for understanding their mechanisms.
  • Current inference techniques struggle to incorporate valuable expert knowledge, potentially leading to inaccuracies.
  • Expert knowledge, often derived from biological interventions, holds significant information for refining network models.

Purpose of the Study:

  • To develop an expert-enabled inference method for biological networks.
  • To address the limitation of existing methods in incorporating expert knowledge.
  • To improve the accuracy of network inference by optimally quantifying expert insights alongside data.

Main Methods:

  • Modeling regulatory networks using Boolean networks with perturbation.
  • Developing an expert-enabled inference approach to estimate unknown network parameters.
  • Quantifying expert knowledge using data-acquiring objectives and confidence levels, integrated with temporal data.

Main Results:

  • The proposed method effectively incorporates expert knowledge into the network inference process.
  • Numerical experiments demonstrated the method's performance on the p53-MDM2 gene regulatory network.
  • The approach optimally balances expert insights with temporal data for enhanced inference.

Conclusions:

  • Integrating expert knowledge significantly improves the accuracy of biological network inference.
  • The developed expert-enabled method offers a novel solution for complex systems biology challenges.
  • This work advances the field of computational biology by providing a more robust inference framework.