ArticlePloS one2017
A novel mutual information-based Boolean network inference method from time-series gene expression data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 78 citations in OpenAlex.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 2025Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- Article
- LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.Briefings in bioinformatics · 2024Article
- SAILoR: Structure-Aware Inference of Logic Rules.PloS one · 2024Article
- LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- Boolean network sketches: a unifying framework for logical model inference.Bioinformatics (Oxford, England) · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Review
- Review and assessment of Boolean approaches for inference of gene regulatory networks.Heliyon · 2022Review
- From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Inference of regulatory networks through temporally sparse data.Frontiers in control engineering · 2022Article
- A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data.Bioinformatics (Oxford, England) · 2021Article
- NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.G3 (Bethesda, Md.) · 2020Article
- HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model.International journal of molecular sciences · 2018Article
- Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm.Genes · 2018Article
- Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks.Frontiers in genetics · 2018Article
- A robust gene regulatory network inference method base on Kalman filter and linear regression.PloS one · 2018Article
- Cattle genome-wide analysis reveals genetic signatures in trypanotolerant N'Dama.BMC genomics · 2017Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundInferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.
resultsIn this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI) method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.
conclusionsTaken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.
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