Evidence map›Paper›PMID 34765096›Full record

ReviewComputational and structural biotechnology journal2021

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists.

Aurélien Quillet, Youssef Anouar, Thierry Lecroq, Christophe Dubessy

Abstract readReview
In one paragraph

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Article
  2. miRNA Target Prediction: An Overview of the Past and Current Tools.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Guidelines for mitochondrial RNA analysis.Molecular therapy. Nucleic acids · 2024
    Review
  10. Article
  11. Frontiers in microbiology · 2024
    Article
  12. Article
  13. Review
  14. Article
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

4 authors.

Aurélien QuilletNormandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.
Youssef AnouarNormandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.
Thierry LecroqNormandie Université, UNIROUEN, UNIHAVRE, INSA Rouen, Laboratoire d'Informatique du Traitement de l'Information et des Systèmes, 76000 Rouen, France.
Christophe DubessyNormandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Indexed as

Bioinformatics toolsComputational prediction methodsData combinationMicroRNAPerformance evaluationTarget prediction

Identifiers

PMID34765096
PMCPMC8567327

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.