Evidence map›Paper›PMID 29194489›Full record

ArticleNucleic acids research2018

mirDIP 4.1-integrative database of human microRNA target predictions.

Tomas Tokar, Chiara Pastrello, Andrea E M Rossos, Mark Abovsky, Anne-Christin Hauschild, Mike Tsay, Richard Lu, Igor Jurisica

Abstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 353 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
353citing papers in PubMed, 2 pooled it
–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

353 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. EML4-ALK fusion rewires transcriptomic, miRNA, and CAF-associated programs in non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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  18. miRNA Target Prediction: An Overview of the Past and Current Tools.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  19. Construction and Analysis of miRNA Regulatory Networks.Methods in molecular biology (Clifton, N.J.) · 2026
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  20. Article

293 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

8 authors.

Tomas TokarKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Chiara PastrelloKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Andrea E M RossosKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Mark AbovskyKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Anne-Christin HauschildKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Mike TsayKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Richard LuKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.
Igor JurisicaKrembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

Indexed as

Databases, GeneticHumansMicroRNAsRNA, MessengerMicroRNAsRNA, Messenger

Identifiers

PMID29194489
PMCPMC5753284

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.