Evidence map›Paper›PMID 23720668›Full record

ArticleFrontiers in genetics2013

A Review of Computational Tools in microRNA Discovery.

Clarissa P C Gomes, Ji-Hoon Cho, Leroy Hood, Octávio L Franco, Rinaldo W Pereira, Kai Wang

Abstract read
In one paragraph

Article in Frontiers in genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. MicroRNA-4463 facilitates the development of colon cancer by suppression of the expression of PPP1R12B.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022
    Article
  5. Computational Detection of Pre-microRNAs.Methods in molecular biology (Clifton, N.J.) · 2022
    Review
  6. Article
  7. A Novel miRNA Located in theBioMed research international · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2021
    Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Review
  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.

Clarissa P C GomesInstitute for Systems Biology Seattle, WA, USA ; Pós-Graduaçao em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília Brasília, Brazil ; Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília Brasília, Brazil.
Ji-Hoon Cho
Leroy Hood
Octávio L Franco
Rinaldo W Pereira
Kai Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as next-generation sequencing significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, we aim to summarize several commonly used tools in metazoan miRNA discovery.

Indexed as

isomermachine learningmiRNA conservationRNA secondary structuresequence homology

Identifiers

PMID23720668
PMCPMC3654206

What OpenQuestion holds

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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.