ReviewCurrent opinion in genetics & development2016
Understanding transcriptional regulatory networks using computational models.
Review in Current opinion in genetics & development, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- Article
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- Global transcription machinery engineering in Yarrowia lipolytica.FEMS yeast research · 2025Review
- Article
- Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.Briefings in bioinformatics · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- An Interplay between Transcription Factors and Recombinant Protein Synthesis inInternational journal of molecular sciences · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- A genome scale transcriptional regulatory model of the human placenta.Science advances · 2024Article
- Alveolar epithelial progenitor cells require Nkx2-1 to maintain progenitor-specific epigenomic state during lung homeostasis and regeneration.Nature communications · 2023Article
- Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks.Scientific reports · 2022Article
- Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa.Nucleic acids research · 2022Article
- Transcriptional Plasticity Drives Leukemia Immune Escape.Blood cancer discovery · 2022Article
- Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.Nucleic acids research · 2022Article
- Computational identification of new potential transcriptional partners of ERRα in breast cancer cells: specific partners for specific targets.Scientific reports · 2022Article
- A systems-level gene regulatory network model for Plasmodium falciparum.Nucleic acids research · 2021Article
- Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.Briefings in bioinformatics · 2021Article
- Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study.NPJ systems biology and applications · 2021Article
- A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.BMC bioinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship.
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Registered trials
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 OpenQuestion graph.