EDBT 2026 Demo / reviewers in the wild / expert
Ieva Kerseviciute
dblp:370/4588
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-9353-038XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
pathway enrichment analysis |
0.7 | 1 | 2023 | aPEAR: an R package for autonomous visualization of pathway enrichment networks · Bioinform. 2023 |
Visualization and visual analytics › biological data visualization
biological network visualization |
0.7 | 1 | 2023 | aPEAR: an R package for autonomous visualization of pathway enrichment networks · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
network clustering · 1.3gene set similarity · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | aPEAR: an R package for autonomous visualization of pathway enrichment networksabstractSUMMARY: The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. AVAILABILITY AND IMPLEMENTATION: The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR). Ieva Kerseviciute, Juozas Gordevicius |
Bioinform. | 1 |