Demo Data from EDBT 2026 / reviewers in the wild / expert
Niels Benjamin Paul
dblp:424/7855
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
gene expression prediction |
0.9 | 1 | 2025 | Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization · Bioinform. 2025 |
Bioinformatics and computational biology › gene regulation
transcriptional regulation analysis |
0.3 | 1 | 2025 | Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization · Bioinform. 2025 |
Methods — techniques the papers use, named apart from their topics
particle swarm optimization · 0.9machine learning · 0.9feature explanation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Prediction of gene expression using histone modification patterns extracted by Particle Swarm OptimizationabstractMOTIVATION: Histone modifications play an important role in transcription regulation. Although the general importance of some histone modifications for transcription regulation has been previously established, the relevance of others and their interaction is subject to ongoing research. By training Machine Learning models to predict a gene's expression and explaining their decision making process, we can get hints on how histone modifications affect transcription. In previous studies, trained models were either hardly explainable or the models were trained solely on the abundance of histone modifications. Based on other studies, which used histone modification patterns, rather than their abundance, to identify potential regulatory elements, we hypothesize the histone modification pattern in a gene's promoter to be more predictive for gene expression. We used an optimization algorithm to extract predictive histone modification profiles. RESULTS: Our algorithm called PatternChrome achieved an average area under curve (AUC) score of 0.9029 over 56 samples for binary classification, outperforming all previous algorithms for the same task. We explained the models decisions to deduce the effect of specific features, certain histone modifications or promoter positions on transcription regulation. Although the predictive histone modification patterns were extracted for each sample separately, they can be used to predict gene expression in other samples, implying that the created patterns are largely generalizable. Interestingly, the impact of histone modifications on gene regulation appears predominantly indifferent to cellular specificity. Through explanation of the classifier's decisions, we substantiate established literature knowledge while concurrently revealing novel insights into the intricate landscape of transcriptional regulation via histone modification. AVAILABILITY AND IMPLEMENTATION: The code for the PatternChrome algorithm, the scripts for the analyses and the required data can be found at (https://gitlab.gwdg.de/MedBioinf/generegulation/patternchrome). Niels Benjamin Paul, Jonas C. Wolber, Malte Lennart Sahrhage, Tim Beißbarth, Martin Haubrock |
Bioinform. | 1 |