ArticleBMC genomics2017
An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection.
Article in BMC genomics, 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.
- Molecular Network Analysis in Model and Non-Model Legumes: Challenges in Omics Data Interpretation Across Species, with a Focus onPlants (Basel, Switzerland) · 2025Review
- Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution.BMC bioinformatics · 2025Article
- Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules.BME frontiers · 2025Article
- Review
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- Disease-associated dysbiosis and potential therapeutic role of Akkermansia muciniphila, a mucus degrading bacteria of gut microbiome.Folia microbiologica · 2022Review
- Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks.Scientific reports · 2022Article
- Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.PLoS computational biology · 2022Article
- Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks.Briefings in bioinformatics · 2022Article
- DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.Briefings in bioinformatics · 2021Article
- Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data.Nucleic acids research · 2021Article
- Synthetic data generation with probabilistic Bayesian Networks.Mathematical biosciences and engineering : MBE · 2021Article
- A new Bayesian piecewise linear regression model for dynamic network reconstruction.BMC bioinformatics · 2021Article
- An Early Stage Researcher's Primer on Systems Medicine Terminology.Network and systems medicine · 2021Review
- The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach.The EPMA journal · 2020Review
- Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases.The Journal of clinical investigation · 2020Review
- Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease.Genes · 2019Review
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
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6 authors.
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Abstract
backgroundThe reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics. However, Bayesian network methods are time consuming and cannot handle large-scale networks due to their high computational complexity, while the mutual information-based methods are highly effective but directionless and have a high false-positive rate.
resultsTo solve these problems, we propose a Candidate Auto Selection algorithm (CAS) based on mutual information and breakpoint detection to restrict the search space in order to accelerate the learning process of Bayesian network. First, the proposed CAS algorithm automatically selects the neighbor candidates of each node before searching the best structure of GRN. Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses on faster learning the structure with little loss of quality.
conclusionResults show that the proposed CAS algorithm can effectively reduce the search space of Bayesian networks through identifying the neighbor candidates of each node. In our experiments, the CAS + G method outperforms the state-of-the-art method on simulation data for inferring GRNs, and the CAS + L method is significantly faster than the state-of-the-art method with little loss of accuracy. Hence, the CAS based methods effectively decrease the computational complexity of Bayesian network and are more suitable for GRN inference.
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