Demo Data from EDBT 2026 / reviewers in the wild / expert
Miho Uematsu
dblp:370/5663
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › network bioinformatics
biological network analysis |
0.7 | 1 | 2023 | ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023 |
Bioinformatics and computational biology › systems bioinformatics › pathway analysis
pathway visualization |
0.7 | 1 | 2023 | ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023 |
Bioinformatics and computational biology › computational microbiology
microbiome analysis |
0.2 | 1 | 2023 | ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023 |
Bioinformatics and computational biology › single-cell analysis
single-cell transcriptomics |
0.2 | 1 | 2023 | ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
grammar of graphics · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphicsabstractSUMMARY: The 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. Noriaki Sato, Miho Uematsu, Kosuke Fujimoto, Satoshi Uematsu, Seiya Imoto |
Bioinform. | 2 |