VLDB 2026 Research / reviewers in the wild / expert
Anela Tosevska
dblp:431/1741
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-0892-7068ORCID · reported
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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › functional genomics
functional enrichment analysis |
1.0 | 1 | 2026 | SummArIzeR: simplifying cross-database enrichment result clustering and annotation via large language models · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.0clustering · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | SummArIzeR: simplifying cross-database enrichment result clustering and annotation via large language modelsabstractMOTIVATION: Enrichment analysis across multiple databases often results in a high level of redundancy due to overlapping terms, complicating the interpretation of biological data. To address this, we developed SummArIzeR, an R package to cluster and annotate enrichment results across multiple databases, enabling fast, intuitive interpretation and comparison across multiple conditions. SummArIzeR clusters enrichment results based on shared genes, calculates a pooled P-value for each cluster and facilitates the cluster annotation using large-language models. It further allows an easily interpretable visualization of the results. RESULTS: Compared to existing tools, SummArIzeR provides unbiased and fast cluster annotation using large language models. We demonstrate that SummArIzeR achieves clustering comparable to manual curation while offering superior grouping based on shared underlying genes. AVAILABILITY AND IMPLEMENTATION: The SummArIzeR package is available as an open-source R package, with a comprehensive user manual provided in its GitHub repository: https://github.com/bonellilab/SummArIzeR. Marie Brinkmann, Michael Bonelli, Anela Tosevska |
Bioinform. | 3 |