Evidence map›Paper›PMID 37846038›Full record

ArticleBioinformatics (Oxford, England)2023

ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics.

Noriaki Sato, Miho Uematsu, Kosuke Fujimoto, Satoshi Uematsu, Seiya Imoto

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
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  3. Biochemistry and biophysics reports · 2026
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  12. Resolving taxonomic uncertainties in the genusFrontiers in microbiology · 2026
    Article
  13. Article
  14. Inhibition ofMicroorganisms · 2025
    Article
  15. Article
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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.

Noriaki SatoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.ORCID 0000-0001-7721-9359
Miho UematsuDepartment of Immunology and Genomics, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan.
Kosuke FujimotoDepartment of Immunology and Genomics, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan.
Satoshi UematsuDepartment of Immunology and Genomics, Graduate School of Medicine, Osaka Metropolitan University, Osaka 545-8585, Japan.
Seiya ImotoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

summaryThe Kyoto Encyclopedia of Genes and Genomes (KEGG) database serves as a valuable systems biology resource and is widely utilized in diverse research fields. However, existing software does not allow flexible visualization and network analyses of the vast and complex KEGG data. We developed ggkegg, an R package that integrates KEGG information with ggplot2 and ggraph. ggkegg enables enhanced visualization and network analyses of KEGG data. We demonstrate the utility of the package by providing examples of its application in single-cell, bulk transcriptome, and microbiome analyses. ggkegg may empower researchers to analyze complex biological networks and present their results effectively. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at: https://github.com/noriakis/ggkegg.

Indexed as

GenomeSoftwareDocumentation

Identifiers

PMID37846038
PMCPMC10612400

What OpenQuestion holds

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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.