Demo Data from EDBT 2026 / reviewers in the wild / expert
Jil Sander
dblp:197/8522
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—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
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
differential expression analysis |
0.3 | 1 | 2017 | ImpulseDE: detection of differentially expressed genes in time series data using impulse models · Bioinform. 2017 |
Bioinformatics and computational biology › gene expression analysis
time series gene expression |
0.3 | 1 | 2017 | ImpulseDE: detection of differentially expressed genes in time series data using impulse models · Bioinform. 2017 |
Bioinformatics and computational biology
transcriptomics |
0.1 | 1 | 2017 | ImpulseDE: detection of differentially expressed genes in time series data using impulse models · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
multithreading · 0.3impulse model · 0.3clustering · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | ImpulseDE: detection of differentially expressed genes in time series data using impulse modelsabstractSummary: Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data. Availability and Implementation: ImpulseDE is available on Bioconductor ( https://bioconductor.org/packages/ImpulseDE/ ). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online. Jil Sander, Joachim L. Schultze, Nir Yosef |
Bioinform. | 1 |