Evidence map›Paper›PMID 33245771›Full record

ArticleNucleic acids research2021

MarkerDB: an online database of molecular biomarkers.

David S Wishart, Brendan Bartok, Eponine Oler, Kevin Y H Liang, Zachary Budinski, Mark Berjanskii, AnChi Guo, Xuan Cao, Michael Wilson

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing 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

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

  1. Knowledge-Driven Approaches to Create the MTox700+ Metabolite Panel for Predicting Toxicity.Toxicological sciences : an official journal of the Society of Toxicology · 2022
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. ATF4 in proximal tubules modulates kidney function and modifies the metabolome.Journal of molecular medicine (Berlin, Germany) · 2025
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Decision Tree for Protein Biomarker Selection for Clinical Applications.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  20. Review

5 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

9 authors.

David S WishartDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Brendan BartokDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Eponine OlerDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Kevin Y H LiangDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Zachary BudinskiDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Mark BerjanskiiDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
AnChi GuoDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Xuan CaoDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Michael WilsonDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MarkerDB is a freely available electronic database that attempts to consolidate information on all known clinical and a selected set of pre-clinical molecular biomarkers into a single resource. The database includes four major types of molecular biomarkers (chemical, protein, DNA [genetic] and karyotypic) and four biomarker categories (diagnostic, predictive, prognostic and exposure). MarkerDB provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function. Currently, the database contains 142 protein biomarkers, 1089 chemical biomarkers, 154 karyotype biomarkers and 26 374 genetic markers. These are categorized into 25 560 diagnostic biomarkers, 102 prognostic biomarkers, 265 exposure biomarkers and 6746 predictive biomarkers or biomarker panels. Collectively, these markers can be used to detect, monitor or predict 670 specific human conditions which are grouped into 27 broad condition categories. MarkerDB is available at https://markerdb.ca.

Indexed as

Databases, FactualGenetic MarkersBiomarkersChromosome AberrationsDiseaseHumansInternetKaryotypingPredictive Value of TestsPrognosisProteinsROC CurveSoftwareBiomarkersGenetic MarkersProteins

Identifiers

PMID33245771
PMCPMC7778954

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.