Evidence map›Paper›PMID 25364745›Full record

ReviewFrontiers in cell and developmental biology2014

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks.

Frank Emmert-Streib, Matthias Dehmer, Benjamin Haibe-Kains

Abstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

111 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Role of theToxins · 2025
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  7. Review
  8. Article
  9. Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Augusta: From RNA-Seq to gene regulatory networks and Boolean models.Computational and structural biotechnology journal · 2024
    Article
  17. From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.Journal of computational biology : a journal of computational molecular cell biology · 2024
    Article
  18. Article
  19. Article
  20. Investigating the Impact of Flavonoids onJournal of fungi (Basel, Switzerland) · 2024
    Article

51 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Frank Emmert-StreibComputational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK.
Matthias DehmerInstitute for Bioinformatics and Translational Research, UMIT Hall in Tyrol, Austria.
Benjamin Haibe-KainsBioinformatics and Computational Genomics Laboratory, Department of Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine.

Indexed as

biomarkercomputational genomicsgene regulatory networksnetwork analysispersonalized medicinestatistical inferencesystems biology

Identifiers

PMID25364745
PMCPMC4207011

What OpenQuestion holds

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Registered trials

None linked

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.