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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Updated: Nov 7, 2025

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MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction.

Wenying He1, Jijun Tang1, Quan Zou2

  • 1Tianjin University, China.

Briefings in Bioinformatics
|May 3, 2021
PubMed
Summary

We developed a novel computational method, Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction (MMFGRN), to accurately infer gene regulatory networks from complex biological data. MMFGRN demonstrates superior performance and robustness across different network scales.

Keywords:
fusion strategygene regulatory networkmachine learningnetwork inference

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) control crucial biological processes, including cell growth, differentiation, and disease development.
  • Determining gene-gene relationships from gene expression data is challenging, necessitating advanced computational approaches.
  • Existing methods often struggle with limited datasets and diverse data types.

Purpose of the Study:

  • To propose a novel computational method, Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction (MMFGRN), for improved GRN inference.
  • To leverage diverse data types (time series and steady-state) for more comprehensive GRN reconstruction.
  • To enhance the accuracy and robustness of GRN inference, particularly without prior biological knowledge.

Main Methods:

  • Developed the MMFGRN model integrating three perspectives: single time series data, single steady-state data, and joint time series/steady-state data models.
  • Employed a weighted fusion strategy to combine results from different models for a final global regulatory link ranking.
  • Validated the method on benchmark datasets (DREAM4 InSilico_Size10 and Size100) for performance evaluation.

Main Results:

  • MMFGRN achieved superior performance on the DREAM4 InSilico_Size10 dataset, outperforming existing algorithms with an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.909 and an Area Under the Precision-Recall curve (AUPR) of 0.770.
  • The method demonstrated robustness and maintained advantages on larger networks, achieving an AUPR of 0.335 on the DREAM4 InSilico_Size100 dataset.
  • The fusion strategy effectively utilized limited datasets and explored potential regulatory relationships across different data types.

Conclusions:

  • MMFGRN offers a robust and accurate approach for reconstructing gene regulatory networks.
  • The multi-source, multi-model fusion strategy provides a novel framework for biological network modeling without prior knowledge.
  • This method aids researchers in deciphering complex life mechanisms and understanding disease pathogenesis.