ArticleBMC systems biology2015
Reconstruction of gene networks using prior knowledge.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.BMC bioinformatics · 2026Article
- NeXus: An Automated Platform for Network Pharmacology and Multi-Method Enrichment Analysis.International journal of molecular sciences · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- Identifying large-scale interaction atlases using probabilistic graphs and external knowledge.Journal of clinical and translational science · 2022Article
- Application of graphical lasso in estimating network structure in gene set.Annals of translational medicine · 2020Article
- A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.BMC bioinformatics · 2020Article
- The single-cell eQTLGen consortium.eLife · 2020Article
- Robust network inference using response logic.Bioinformatics (Oxford, England) · 2019Article
- Hierarchical parameter estimation of GRN based on topological analysis.IET systems biology · 2018Article
- Network Modeling Unravels Mechanisms of Crosstalk between Ethylene and Salicylate Signaling in Potato.Plant physiology · 2018Article
- NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources.Bioinformatics (Oxford, England) · 2018Article
- BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research.Scientific reports · 2016Article
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3 authors.
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Abstract
backgroundReconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.
resultsIn this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.
conclusionPriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.
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