ArticleBMC genomics2019
D3GRN: a data driven dynamic network construction method to infer gene regulatory networks.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- DANSE: a pipeline for dynamic modelling of time-series multi-omics data.BMC bioinformatics · 2025Article
- NeXus: An Automated Platform for Network Pharmacology and Multi-Method Enrichment Analysis.International journal of molecular sciences · 2025Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.Communications biology · 2022Article
- Inferring causal gene regulatory networkFrontiers in bioengineering and biotechnology · 2022Article
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Authors and funding
5 authors.
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Abstract
backgroundTo infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem.
resultsIn this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR.
conclusionsWe have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.
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