Demo Data from EDBT 2026 / reviewers in the wild / expert
David J. N. P. Rasp
dblp:235/6653
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-0164-2163ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › network bioinformatics › biological network analysis
functional module identification |
0.4 | 1 | 2019 | MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions · Bioinform. 2019 |
Bioinformatics and computational biology
metabolomics |
0.4 | 1 | 2019 | MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
correlation network analysis · 0.4clustering · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | MoDentify: phenotype-driven module identification in metabolomics networks at different resolutionsabstractSummary: Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online. Kieu Trinh Do, David J. N. P. Rasp, Gabi Kastenmüller, Karsten Suhre, Jan Krumsiek |
Bioinform. | 2 |