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Multi-objective consensus optimization for gene regulatory networks inference: A preference-based approach.

Adrián Segura-Ortiz1, Antonio J Nebro1, José García-Nieto2

  • 1Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain.

Computational Biology and Chemistry
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

A new preference-guided evolutionary algorithm, PBEvoGen, enhances gene regulatory network inference by integrating biological knowledge. It improves accuracy and efficiency over existing methods, particularly for large-scale biological networks.

Keywords:
BioinformaticsEvolutionary algorithmsGene regulatory networksInferencePreference-based selection

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding biological processes and disease.
  • Inferring GRNs from expression data is a fundamental challenge in computational biology.
  • Existing GRN inference methods often suffer from domain biases and lack biological knowledge integration, limiting their in-vivo applicability.

Purpose of the Study:

  • To develop a novel approach for GRN inference that addresses limitations of existing methods.
  • To integrate biological knowledge and a preference-guided selection mechanism into a multi-objective evolutionary algorithm.
  • To improve the accuracy, efficiency, and biological relevance of inferred GRNs.

Main Methods:

  • Proposed a preference-guide selection mechanism to direct search towards biologically relevant regions.
  • Integrated this mechanism into MO-GENECI, a multi-objective evolutionary algorithm (PBEvoGen).
  • Evaluated PBEvoGen on 43 GRNs from DREAM3, DREAM4 benchmarks, and TFLink database using AUROC and AUPR metrics.

Main Results:

  • PBEvoGen outperformed the original MO-GENECI algorithm in network quality and accuracy.
  • Achieved mean AUROC of 0.67 and AUPR of 0.23 across 43 benchmark networks.
  • Demonstrated improved performance, reduced computational effort, and enhanced accuracy, especially for large networks.

Conclusions:

  • The combination of expert knowledge and evolutionary algorithms provides a robust and efficient methodology for GRN inference.
  • PBEvoGen offers a significant advancement in inferring gene regulatory networks with improved accuracy and biological relevance.
  • The developed software is publicly available via GitHub and PyPI for broader accessibility and application.