ArticleScientific reports2016
BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- RFX5 promotes the progression of triple-negative breast cancer through transcriptional activation of JAG1.Human cell · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets.Scientific reports · 2022Article
- KBoost: a new method to infer gene regulatory networks from gene expression data.Scientific reports · 2021Article
- Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq.Frontiers in bioinformatics · 2021Article
- Comparative analysis of differential gene expression tools for RNA sequencing time course data.Briefings in bioinformatics · 2019Article
- PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation.Frontiers in plant science · 2019Article
- BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data.BMC systems biology · 2018Article
- A robust gene regulatory network inference method base on Kalman filter and linear regression.PloS one · 2018Article
- Identification of four differentially methylated genes as prognostic signatures for stage I lung adenocarcinoma.Cancer cell international · 2018Article
- Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.Scientific reports · 2017Article
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3 authors.
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Abstract
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.
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