Evidence map›Paper›PMID 37589308›Full record

ArticleGigaScience2022

Lessons learned to boost a bioinformatics knowledge base reusability, the Bgee experience.

Tarcisio Mendes de Farias, Julien Wollbrett, Marc Robinson-Rechavi, Frederic Bastian

Abstract read
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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.

Tarcisio Mendes de FariasSIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.ORCID 0000-0002-3175-5372
Julien WollbrettSIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.ORCID 0000-0002-3099-3117
Marc Robinson-RechaviSIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.ORCID 0000-0002-3437-3329
Frederic BastianSIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.ORCID 0000-0002-9415-5104

Funding

Integration of comprehensive cancer mutation and expression-associated data for biomarker evaluation and discoveryU01CA215010 · NCI · GEORGE WASHINGTON UNIVERSITY · PI CRICHTON, DANIEL, MAZUMDER, RAJA · 2017 to 2019
$1.3M
NCI NIH HHS U01 CA215010
6 · The paper itself

Abstract

backgroundEnhancing interoperability of bioinformatics knowledge bases is a high-priority requirement to maximize data reusability and thus increase their utility such as the return on investment for biomedical research. A knowledge base may provide useful information for life scientists and other knowledge bases, but it only acquires exchange value once the knowledge base is (re)used, and without interoperability, the utility lies dormant.

resultsIn this article, we discuss several approaches to boost interoperability depending on the interoperable parts. The findings are driven by several real-world scenario examples that were mostly implemented by Bgee, a well-established gene expression knowledge base. To better justify the findings are transferable, for each Bgee interoperability experience, we also highlight similar implementations by major bioinformatics knowledge bases. Moreover, we discuss ten general main lessons learned. These lessons can be applied in the context of any bioinformatics knowledge base to foster data reusability.

conclusionsThis work provides pragmatic methods and transferable skills to promote reusability of bioinformatics knowledge bases by focusing on interoperability.

Indexed as

Biomedical ResearchComputational BiologyKnowledge Basesdatabasesdata reusabilityinteroperabilityontologies

Identifiers

PMID37589308
PMCPMC10433096

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.