ReviewComputational and structural biotechnology journal2021
Computational biology approaches for mapping transcriptional regulatory networks.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Trial
- Article
- Hif-1 responsive IFFLs to explain specific transcriptional responses to cycling hypoxia in cancers.NPJ systems biology and applications · 2025Article
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- Gene regulation technologies for gene and cell therapy.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Unraveling the multifaceted roles of SPL transcription factors in leaf development.Frontiers in plant science · 2025Review
- Single-Cell RNA-seq Analysis Reveals a Positive Correlation between Ferroptosis and Beta-Cell Dedifferentiation in Type 2 Diabetes.Biomedicines · 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
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Growth-regulating factor 15-mediated gene regulatory network enhances salt tolerance in poplar.Plant physiology · 2023Article
- Cracking the Code of Neuronal Cell Fate.Cells · 2023Review
- Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods.Computational and structural biotechnology journal · 2023Review
- Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties ofMicroorganisms · 2022Review
- E2F2 promotes lung adenocarcinoma progression through B-Myb- and FOXM1-facilitated core transcription regulatory circuitry.International journal of biological sciences · 2022Article
- Mr.Vc v2: An updated version of database with increased data of transcriptome and experimental validated interactions.Frontiers in microbiology · 2022Article
- Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks.Computational and structural biotechnology journal · 2022Article
- The variation in promoter sequences of the Akt3 gene between cow and buffalo revealed different responses against mastitis.Journal, genetic engineering & biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.
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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.