Evidence map›Paper›PMID 30670076›Full record

ArticleGenome biology2019

Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data.

Weijun Liu, Xiaowei Wang

Abstract readValidation Study
In one paragraph

Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 539 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
539citing papers in PubMed, 1 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

539 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. PCBP2 facilitates miR-93-5p-mediated repression ofMolecular therapy. Nucleic acids · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

479 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

2 authors.

Weijun LiuDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
Xiaowei WangDepartment of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA. xiaowei.wang@wustl.edu.ORCID 0000-0001-9447-4685

Funding

COMBINED COMPUTATIONAL AND EXPERIMENTAL ANALYSES OF GENE REGULATION BY MICRORNAS.R01GM089784 · NIGMS · WASHINGTON UNIVERSITY · PI WANG, XIAOWEI · 2010 to 2019
$2.8M
MICRORNA BIOMARKERS FOR OROPHARYNGEAL CANCERR01DE026471 · NIDCR · WASHINGTON UNIVERSITY · PI WANG, XIAOWEI · 2017 to 2021
$2.2M
Rational design of small interfering RNA for genome-wide target knockdownR41HG008567 · NHGRI · NAWGEN, LLC · PI LIU, WEIJUN, WANG, XIAOWEI · 2015 to 2016
$697k
NHGRI NIH HHS R41 HG008567NIDCR NIH HHS R01 DE026471NIGMS NIH HHS R01 GM089784NIH HHS R01GM089784, R01DE026471, R41HG008567
6 · The paper itself

Abstract

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).

Indexed as

Gene Expression RegulationModels, GeneticDown-RegulationGene ExpressionMicroRNAsSequence Analysis, RNAMicroRNAsCLIP-seqMicroRNARNA-seqTarget prediction

Identifiers

PMID30670076
PMCPMC6341724

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.