Demo Data from VLDB 2026 / reviewers in the wild / expert
Dickson Aruhomukama
dblp:419/9643
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0003-2818-7682ORCID · 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% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › computational microbiology
antimicrobial resistance prediction |
0.9 | 1 | 2025 | Balancing complexity and clarity - towards clinician-ready antibiotic resistance prediction models · Bioinform. 2025 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Balancing complexity and clarity - towards clinician-ready antibiotic resistance prediction modelsabstractMOTIVATION: The escalating challenge of antibiotic resistance (ABR) demands clinician-ready machine learning models that are not only accurate but interpretable. RESULTS: By treating resistance genes as independent features and augmenting them with curated single-nucleotide polymorphisms and contextual markers, this approach delivers scalable, transparent predictions aligned with clinical decision-making needs. AVAILABILITY AND IMPLEMENTATION: Not applicable. Dickson Aruhomukama |
Bioinform. | 1 |