Evidence map›Paper›PMID 26030752›Full record

ArticlePloS one2015

Dietary MicroRNA Database (DMD): An Archive Database and Analytic Tool for Food-Borne microRNAs.

Kevin Chiang, Jiang Shu, Janos Zempleni, Juan Cui

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. 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
1.8field-weighted citation impact, top 17% 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, 34 citations in OpenAlex.

  1. Article
  2. Review
  3. miRNA therapeutics in precision oncology: a natural premium to nurture.Exploration of targeted anti-tumor therapy · 2022
    Review
  4. Review
  5. Dietary microRNA-A Novel Functional Component of Food.Advances in nutrition (Bethesda, Md.) · 2019
    Review
  6. Article
  7. Article
  8. Non-coding RNAs and Their Roles in Stress Response in Plants.Genomics, proteomics & bioinformatics · 2017
    Review
  9. Article
  10. Nutrition, microRNAs, and Human Health.Advances in nutrition (Bethesda, Md.) · 2017
    Review
  11. Article
  12. Review
  13. Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2015
    Article
  14. 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

4 authors at 1 institution in 1 country.

Kevin ChiangDepartment of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States of America.
Jiang ShuDepartment of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States of America.
Janos ZempleniDepartment of Nutrition and Health Sciences, University of Nebraska-Lincoln, Lincoln, NE, United States of America.
Juan CuiDepartment of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States of America.
University of Nebraska–Lincoln · US

Funding

The role of stress in the fetal origin of obesity and metabolic dysfunctionP20GM104320 · NIGMS · UNIVERSITY OF NEBRASKA LINCOLN · PI ZEMPLENI, JANOS · 2014 to 2024
$24.9M
NIGMS NIH HHS P20 GM104320NIGMS NIH HHS P20GM104320
6 · The paper itself

Abstract

With the advent of high throughput technology, a huge amount of microRNA information has been added to the growing body of knowledge for non-coding RNAs. Here we present the Dietary MicroRNA Databases (DMD), the first repository for archiving and analyzing the published and novel microRNAs discovered in dietary resources. Currently there are fifteen types of dietary species, such as apple, grape, cow milk, and cow fat, included in the database originating from 9 plant and 5 animal species. Annotation for each entry, a mature microRNA indexed as DM0000*, covers information of the mature sequences, genome locations, hairpin structures of parental pre-microRNAs, cross-species sequence comparison, disease relevance, and the experimentally validated gene targets. Furthermore, a few functional analyses including target prediction, pathway enrichment and gene network construction have been integrated into the system, which enable users to generate functional insights through viewing the functional pathways and building protein-protein interaction networks associated with each microRNA. Another unique feature of DMD is that it provides a feature generator where a total of 411 descriptive attributes can be calculated for any given microRNAs based on their sequences and structures. DMD would be particularly useful for research groups studying microRNA regulation from a nutrition point of view. The database can be accessed at http://sbbi.unl.edu/dmd/.

Indexed as

Databases, GeneticDietFoodAnimalsBase SequenceCattleGene Regulatory NetworksHumansMicroRNAsMicroRNAs

Identifiers

PMID26030752
PMCPMC4451068
OpenAlexW650142076

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.