Evidence map›Paper›PMID 36697234›Full record

ReviewMolecules and cells2023

Determinants of Functional MicroRNA Targeting.

Hyeonseo Hwang, Hee Ryung Chang, Daehyun Baek

Abstract readReview
In one paragraph

Review in Molecules and cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
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  6. Lactate-induced miR-7-5p/TRIM33 reprograms metabolic flux to suppress tumor growth and viral reactivation.Molecular therapy : the journal of the American Society of Gene Therapy · 2026
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  7. Article
  8. Review
  9. Article
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  12. MECP2 Insufficiency Attenuates RUNX2-Dependent Osteoblast Differentiation via miR-126-3p/DKK1-Mediated Canonical Wnt Signaling Inhibition in Rett Syndrome.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
  13. Article
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  15. Article
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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.

Hyeonseo HwangSchool of Biological Sciences, Seoul National University, Seoul 08826, Korea.ORCID https://orcid.org/0000-0001-8213-5977
Hee Ryung ChangSchool of Biological Sciences, Seoul National University, Seoul 08826, Korea.ORCID https://orcid.org/0000-0003-0477-5690
Daehyun BaekSchool of Biological Sciences, Seoul National University, Seoul 08826, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

Indexed as

Gene TargetingMicroRNAsComputational BiologyRNA, MessengerMicroRNAsRNA, MessengerbioinformaticsmicroRNAmicroRNA targetingmicroRNA targeting determinantsmicroRNA target prediction

Identifiers

PMID36697234
PMCPMC9880601

What OpenQuestion holds

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LicenceCC BY-NC-SA
Read underepoch 390

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.