ArticleFrontiers in genetics2023
Improving gene regulatory network inference and assessment: The importance of using network structure.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 23 citations in OpenAlex.
- Transcriptomic insights into drought response in wild Arachis relatives A. dardani and A. ipaënsis.BMC plant biology · 2025Article
- Integrative gene co-expression network analysis reveals protein-coding and LncRNA genes associated with Alzheimer's disease pathology.Scientific reports · 2025Article
- Organ-level gene-regulatory networks inferred from transcriptomic data reveal context-specific regulation and highlight novel regulators of ripening and ABA-mediated responses in tomato.Plant communications · 2025Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions inFrontiers in microbiology · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- Predicting fitness inFrontiers in tuberculosis · 2025Article
- Co-option of plant gene regulatory network in nutrient responses during terrestrialization.Nature plants · 2024Article
- Network analyses predict major regulators of resistance to early blight disease complex in tomato.BMC plant biology · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.