Demo Data from EDBT 2026 / reviewers in the wild / expert
Sara L. Dunn
dblp:192/4271
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics › genotype-phenotype association
disease association |
0.2 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Bioinformatics and computational biology
gene expression analysis |
0.2 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction network |
0.1 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
phenotype-gene association · 0.2network analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associationsabstractPhenomeScape is a Cytoscape app which provides easy access to the PhenomeExpress algorithm to interpret gene expression data. PhenomeExpress integrates protein interaction networks with known phenotype to gene associations to find active sub-networks enriched in differentially expressed genes. It also incorporates cross-species phenotypes and associations to include results from animal models of disease. With expression data imported into PhenomeScape, the user can quickly generate and visualise interactive sub-networks. PhenomeScape thus enables researchers to use prior knowledge of a disease to identify differentially regulated sub-networks and to generate an overview of altered biologically processes specific to that disease. AVAILABILITY AND IMPLEMENTATION: Freely available for download at https://github.com/soulj/PhenomeScape CONTACT: [email protected] or [email protected]. Jamie Soul, Sara L. Dunn, Tim E. Hardingham, Ray P. Boot-Handford, Jean-Marc Schwartz |
Bioinform. | 2 |