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Anela Tosevska

dblp:431/1741 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics
functional enrichment analysis
1.012026
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
YearPublicationVenuePosition
2026 SummArIzeR: simplifying cross-database enrichment result clustering and annotation via large language models
abstract
MOTIVATION: 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