Evidence map›Paper›PMID 21107708›Full record

ReviewMolecular biology reports2011

The discovery approaches and detection methods of microRNAs.

Yong Huang, Quan Zou, Sheng Peng Wang, Shun Ming Tang, Guo Zheng Zhang, Xing Jia Shen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Light-up RNA aptamer-based T-NASBA: a one-pot, label-free strategy for miRNA detection.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025
    Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Research advances in the detection of miRNA.Journal of pharmaceutical analysis · 2019
    Review
  9. New insights into tomato microRNAs.Scientific reports · 2018
    Article
  10. Article
  11. Article
  12. Bioinformatic tools for microRNA dissection.Nucleic acids research · 2016
    Review
  13. Small RNA Detection by in Situ Hybridization Methods.International journal of molecular sciences · 2015
    Review
  14. Identifying miRNAs, targets and functions.Briefings in bioinformatics · 2014
    Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Yong HuangJiang Su University of Science and Technology, Zhenjiang, 212018, Jiangsu, People's Republic of China.
Quan Zou
Sheng Peng Wang
Shun Ming Tang
Guo Zheng Zhang
Xing Jia Shen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small, highly conserved, non-coding RNAs that regulate gene expression of target mRNAs through cleavage or translational inhibition. Computer-based approaches for miRNA gene identification are being considered as indispensable in miRNAs research. Similarly, experimental approaches for detection of miRNAs are crucial to the testing and validating of computational algorithms. The detection of miRNAs in tissues or cells can supply valuable information for investigating the biological function of these molecules. Selective and highly sensitive detection methods will pave the way for extended understanding of miRNA function within organisms. In this review, we summarize the various computational methods for identification of miRNAs as well as the methodologies that have been developed to detection miRNAs.

Indexed as

AnimalsComputational BiologyHumansMicroRNAsReverse Transcriptase Polymerase Chain ReactionSequence Analysis, RNAMicroRNAs

Identifiers

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.