Demo Data from VLDB 2026 / reviewers in the wild / expert
Valentine M. Macaulay
dblp:425/3420
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
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 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
enrichment result visualization |
0.9 | 1 | 2025 | GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation · Bioinform. 2025 |
Bioinformatics and computational biology › functional genomics
functional enrichment analysis |
0.9 | 1 | 2025 | GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation · Bioinform. 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretationabstractSUMMARY: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. AVAILABILITY AND IMPLEMENTATION: GeneFEAST GitHub repository: https://github.com/avigailtaylor/GeneFEAST; Zenodo record: 10.5281/zenodo.14753734; Python Package Index: https://pypi.org/project/genefeast; Docker container: ghcr.io/avigailtaylor/genefeast. Avigail Taylor, Valentine M. Macaulay, Matthieu J. Miossec, Anand K. Maurya, Francesca Buffa |
Bioinform. | 2 |