Evidence map›Paper›PMID 36088548›Full record

ArticleBriefings in bioinformatics2022

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments.

Annika L Gable, Damian Szklarczyk, David Lyon, João F Matias Rodrigues, Christian von Mering

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025
    Review
  11. Review
  12. A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. In Silico Models to Validate Novel Blood-Based Biomarkers.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  19. 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

5 authors.

Annika L GableDepartment of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
Damian SzklarczykDepartment of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
David LyonDepartment of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
João F Matias RodriguesDepartment of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.
Christian von MeringDepartment of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.ORCID 0000-0001-7734-9102

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.

Indexed as

GenomicsSoftwareDatabases, FactualDatabases, GeneticHumansbenchmarkfunctional annotationGene Ontologygene set enrichmentpathwaysSTRING

Identifiers

PMID36088548
PMCPMC9487593

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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.