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Evgeny Cheremushkin

dblp:70/2722 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene regulation › gene regulatory network
gene regulatory network analysis
0.112006
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
0.112006
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
YearPublicationVenuePosition
2006 Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations
abstract
MOTIVATION: 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