Evidence map›Paper›PMID 29982332›Full record

ReviewBriefings in bioinformatics2019

Trends in the development of miRNA bioinformatics tools.

Liang Chen, Liisa Heikkinen, Changliang Wang, Yang Yang, Huiyan Sun, Garry Wong

Open access · bronzeAbstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 324 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
324citing papers in PubMed, 1 pooled it
11.5field-weighted citation impact, top 1% 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

324 citing papers in PubMed, 1 synthesis or guideline pooled it, 629 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Identification of theBiology · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. MicroRNAs and Their Profiling via Single-Cell Sequencing Technologies.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  17. Article
  18. Article
  19. Article
  20. Article

264 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

6 authors at 2 institutions in 2 countries.

Liang ChenFaculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China.
Liisa HeikkinenFaculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China.
Changliang WangFaculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China.
Yang YangFaculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China.
Huiyan SunKey Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Garry WongFaculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China.
University of Macau · MOJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

Indexed as

AlgorithmsComputational BiologyMachine LearningMicroRNAsNucleic Acid ConformationMicroRNAsbibliometricbioinformatics toolsmiRNArankingtext mining

Identifiers

PMID29982332
PMCPMC7414524
OpenAlexW2810144884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.