ArticleBioinformatics (Oxford, England)2013
Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed.
- METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.Bioinformatics (Oxford, England) · 2026Article
- Growth-limiting drought increases sensitivity of Asian rice (Oryza sativa) leaves to heat shock through physiological and spatially distinct transcriptomic responses.The Plant journal : for cell and molecular biology · 2025Article
- BiGSM: Bayesian inference of gene regulatory network via sparse modelling.Bioinformatics (Oxford, England) · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- Leveraging dynamic stability to infer regulation in protein-protein interaction networks: A study of infectious vulnerability in COPD.PloS one · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.The Journal of physiology · 2024Article
- Machine learning empowered next generation DNA sequencing: perspective and prospectus.Chemical science · 2024Review
- Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana.Bioinformatics (Oxford, England) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.Bioinformatics (Oxford, England) · 2023Article
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- Network reconstruction for trans acting genetic loci using multi-omics data and prior information.Genome medicine · 2022Article
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- Predicting which genes will respond to transcription factor perturbations.G3 (Bethesda, Md.) · 2022Article
- Statistical challenges in longitudinal microbiome data analysis.Briefings in bioinformatics · 2022Review
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.Bioinformatics (Oxford, England) · 2022Article
- Multi-omics protein-coding units as massively parallel Bayesian networks: Empirical validation of causality structure.iScience · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
15 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
motivationInferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein-protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.
resultsWe developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors. AVAILABILITY AND IMPLEMENTATION: Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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