Evidence map›Paper›PMID 27903911›Full record

ArticleNucleic acids research2017

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

Alireza Ahadi, Gaurav Sablok, Gyorgy Hutvagner

Open access · goldAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

15 citing papers in PubMed, 32 citations in OpenAlex.

  1. An Overview of miRNA and miRNA Target Analysis Tools.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Observational
  8. Review
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Regulation of PTEN expression by noncoding RNAs.Journal of experimental & clinical cancer research : CR · 2018
    Review
  15. 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

3 authors at 1 institution in 1 country.

Alireza AhadiFaculty of Engineering and Information Technology, School of Software, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
Gaurav SablokPlant Functional Biology and Climate Change Cluster (C3), University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
Gyorgy HutvagnerFaculty of Engineering and Information Technology, Centre of Health Technologies, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
University of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.

Indexed as

Computer SimulationGene Expression RegulationModels, GeneticAlgorithmsArgonaute ProteinsBinding SitesCell LineHumansImmunoprecipitationMachine LearningMicroRNAsRNA, MessengerSequence Analysis, RNASoftwareAGO2 protein, humanArgonaute ProteinsMicroRNAsRNA, Messenger

Identifiers

PMID27903911
PMCPMC5389546
OpenAlexW2557808169

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.