Evidence map›Paper›PMID 25147825›Full record

ArticleBioMed research international2014

Studying the complex expression dependences between sets of coexpressed genes.

Mario Huerta, Oriol Casanova, Roberto Barchino, Jose Flores, Enrique Querol, Juan Cedano

Abstract read
In one paragraph

Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mario HuertaInstitut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain.
Oriol CasanovaEscola Tècnica Superior de Ingenieria, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain.ORCID 0000-0002-7581-3220
Roberto BarchinoEscola Tècnica Superior de Ingenieria, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain.
Jose FloresEscola Tècnica Superior de Ingenieria, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain.ORCID 0000-0001-9869-8897
Enrique QuerolInstitut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain.
Juan CedanoLaboratorio de Inmunología, CENUR del Noroeste, Universidad de la República-Salto, Rivera 1350, 50000 Salto, Uruguay.ORCID 0000-0003-1380-8036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

Indexed as

AlgorithmsCell Line, TumorCluster AnalysisDatabases, GeneticGene ExpressionGene Expression ProfilingGene Regulatory NetworksHumans

Identifiers

PMID25147825
PMCPMC4132326

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.