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Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte

Kaixian Yu1, Zihan Cui1, Xin Sui1

  • 1Department of Statistics, Florida State University, Tallahassee, FL, United States.

Frontiers in Genetics
|December 16, 2021
PubMed
Summary
This summary is machine-generated.

We developed GRASP, a novel three-stage Bayesian network (BN) structure learning method. This approach effectively identifies complex biological relationships from genomics data, improving upon existing methods.

Keywords:
Bayesian networkBayesian network structure learningGRASP for BN structure learningadaptive sequential Monte Carlobiological network inferencesequential Monte Carlo

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Bayesian networks (BNs) are powerful tools for modeling complex joint probability distributions.
  • Learning the structure of BNs from data remains a significant challenge, particularly in high-dimensional biological datasets.
  • Existing methods often struggle with accuracy and efficiency in discovering intricate correlation structures.

Purpose of the Study:

  • To introduce a novel, robust, and efficient method for Bayesian network structure learning.
  • To enhance the discovery of complex biological relationships within high-dimensional genomics data.
  • To improve the accuracy and completeness of identified network structures.

Main Methods:

  • A three-stage approach named GRowth-based Approach with Staged Pruning (GRASP) was developed.
  • GRASP utilizes a double filtering strategy for initial skeleton discovery.
  • An adaptive sequential Monte Carlo (adSMC) algorithm refines network structures, followed by an edge reclamation stage.

Main Results:

  • GRASP demonstrated highly satisfactory performance on benchmark networks.
  • The method successfully identified complex correlation structures in simulated and real-world data.
  • The adaptive SMC component improved the quality and diversity of sampled network structures.

Conclusions:

  • GRASP offers a significant advancement in Bayesian network structure learning.
  • The method shows great potential for discovering novel biological relationships in integrative genomic studies.
  • GRASP provides a reliable framework for analyzing complex biological data.