dc.creator | Nepomuceno Chamorro, Isabel de los Ángeles | es |
dc.creator | Azuaje, Francisco | es |
dc.creator | Devaux, Yvan | es |
dc.creator | Nazarov, Petr V. | es |
dc.creator | Muller, Arnaud | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Wagner, Daniel R. | es |
dc.date.accessioned | 2022-07-22T08:16:54Z | |
dc.date.available | 2022-07-22T08:16:54Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Nepomuceno Chamorro, I.d.l.Á., Azuaje, F., Devaux, Y., Nazarov, P.V., Muller, A., Aguilar Ruiz, J.S. y Wagner, D.R. (2011). Prognostic transcriptional association networks: a new supervised approach based on regression trees. Bioinformatics, 27 (2), 252-258. | |
dc.identifier.issn | 1367-4803 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135711 | |
dc.description.abstract | Motivation: The application of information encoded in molecular
networks for prognostic purposes is a crucial objective of systems
biomedicine. This approach has not been widely investigated in
the cardiovascular research area. Within this area, the prediction of
clinical outcomes after suffering a heart attack would represent a
significant step forward. We developed a new quantitative prediction based method for this prognostic problem based on the discovery of
clinically relevant transcriptional association networks. This method
integrates regression trees and clinical class-specific networks, and
can be applied to other clinical domains.
Results: Before analyzing our cardiovascular disease dataset,
we tested the usefulness of our approach on a benchmark
dataset with control and disease patients. We also compared it to
several algorithms to infer transcriptional association networks and
classification models. Comparative results provided evidence of the
prediction power of our approach. Next, we discovered new models
for predicting good and bad outcomes after myocardial infarction.
Using blood-derived gene expression data, our models reported
areas under the receiver operating characteristic curve above 0.70.
Our model could also outperform different techniques based on
co-expressed gene modules. We also predicted processes that may
represent novel therapeutic targets for heart disease, such as the
synthesis of leucine and isoleucine | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2007–68084–C02–00 | es |
dc.format | application/pdf | es |
dc.format.extent | 7 | es |
dc.language.iso | eng | es |
dc.publisher | Oxford Academic | es |
dc.relation.ispartof | Bioinformatics, 27 (2), 252-258. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Prognostic transcriptional association networks: a new supervised approach based on regression trees | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2007–68084–C02–00 | es |
dc.relation.publisherversion | https://academic.oup.com/bioinformatics/article/27/2/252/286006 | es |
dc.identifier.doi | 10.1093/bioinformatics/btq645 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.journaltitle | Bioinformatics | es |
dc.publication.volumen | 27 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 252 | es |
dc.publication.endPage | 258 | es |
dc.identifier.sisius | 6440401 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |