Demo Data from EDBT 2026 / reviewers in the wild / expert
Evgeny Cheremushkin
dblp:70/2722
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 regulation › gene regulatory network
gene regulatory network analysis |
0.1 | 1 | 2006 | Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations · Bioinform. 2006 |
Bioinformatics and computational biology › gene regulation › promoter analysis
promoter modeling |
0.1 | 1 | 2006 | Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
genetic algorithm · 0.1fitness function · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinationsabstractMOTIVATION: Functionally related genes involved in the same molecular-genetic, biochemical or physiological process are often regulated coordinately. Such regulation is provided by precisely organized binding of a multiplicity of special proteins [transcription factors (TFs)] to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. RESULTS: Here we present a new approach for defining promoter models based on the composition of TF binding sites and their pairs. We utilize a multicomponent fitness function for selection of the promoter model that fits best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated and permutated data. AVAILABILITY: The CMA program is freely available for non-commercial users. URL http://www.gene-regulation.com/pub/programs.html#CMAnalyst. It is also a part of the commercial system ExPlain (www.biobase.de) designed for causal analysis of gene expression data.. Alexander E. Kel, Tatiana Konovalova, T. Waleev, Evgeny Cheremushkin, Olga V. Kel-Margoulis, Edgar Wingender |
Bioinform. | 4 |