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Miho Uematsu

dblp:370/5663 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › network bioinformatics
biological network analysis
0.712023
ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023
Bioinformatics and computational biology › systems bioinformatics › pathway analysis
pathway visualization
0.712023
ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023
Bioinformatics and computational biology › computational microbiology
microbiome analysis
0.212023
ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics · Bioinform. 2023
Bioinformatics and computational biology › single-cell analysis
single-cell transcriptomics
0.212023
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
YearPublicationVenuePosition
2023 ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics
abstract
SUMMARY: 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