Evidence map›Paper›PMID 20177143›Full record

ReviewExperimental & molecular medicine2010

Got target? Computational methods for microRNA target prediction and their extension.

Hyeyoung Min, Sungroh Yoon

Open access · goldAbstract readReview
In one paragraph

Review in Experimental & molecular medicine, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 3% of its field
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

106 citing papers in PubMed, 1 synthesis or guideline pooled it, 204 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  8. Article
  9. Heart failure with preserved ejection fraction in pigs causes shifts in posttranscriptional checkpoints.American journal of physiology. Heart and circulatory physiology · 2024
    Article
  10. Article
  11. Article
  12. Review
  13. Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024
    Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

46 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

2 authors at 2 institutions in 1 country.

Hyeyoung MinCollege of Pharmacy, Chung-Ang University, Seoul 156-756, Korea.
Sungroh Yoon
Chung-Ang University · KRKorea University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets, and increasing numbers of computational algorithms and web-based resources are being developed to fulfill the need of scientists performing miRNA research. Here we summarize the rules to predict miRNA targets and introduce some computational algorithms that have been developed for miRNA target prediction and the application of the methods. In addition, the issue of target gene validation in an experimental way will be discussed.

Indexed as

AlgorithmsAnimalsBase SequenceComputational BiologyHumansMicroRNAsMolecular Sequence DataNucleic Acid ConformationNucleic Acid HybridizationSubstrate SpecificityMicroRNAs

Identifiers

PMID20177143
PMCPMC2859323
OpenAlexW2069795758

What OpenQuestion holds

Textmetadata
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.