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Prognostic transcriptional association networks: a new supervised approach based on regression trees

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...

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Autores principales: Nepomuceno-Chamorro, Isabel, Azuaje, Francisco, Devaux, Yvan, Nazarov, Petr V., Muller, Arnaud, Aguilar-Ruiz, Jesús S., Wagner, Daniel R.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018815/
https://www.ncbi.nlm.nih.gov/pubmed/21098433
http://dx.doi.org/10.1093/bioinformatics/btq645
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author Nepomuceno-Chamorro, Isabel
Azuaje, Francisco
Devaux, Yvan
Nazarov, Petr V.
Muller, Arnaud
Aguilar-Ruiz, Jesús S.
Wagner, Daniel R.
author_facet Nepomuceno-Chamorro, Isabel
Azuaje, Francisco
Devaux, Yvan
Nazarov, Petr V.
Muller, Arnaud
Aguilar-Ruiz, Jesús S.
Wagner, Daniel R.
author_sort Nepomuceno-Chamorro, Isabel
collection PubMed
description 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. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-30188152011-01-12 Prognostic transcriptional association networks: a new supervised approach based on regression trees Nepomuceno-Chamorro, Isabel Azuaje, Francisco Devaux, Yvan Nazarov, Petr V. Muller, Arnaud Aguilar-Ruiz, Jesús S. Wagner, Daniel R. Bioinformatics Original Papers 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. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2011-01-15 2010-11-21 /pmc/articles/PMC3018815/ /pubmed/21098433 http://dx.doi.org/10.1093/bioinformatics/btq645 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Nepomuceno-Chamorro, Isabel
Azuaje, Francisco
Devaux, Yvan
Nazarov, Petr V.
Muller, Arnaud
Aguilar-Ruiz, Jesús S.
Wagner, Daniel R.
Prognostic transcriptional association networks: a new supervised approach based on regression trees
title Prognostic transcriptional association networks: a new supervised approach based on regression trees
title_full Prognostic transcriptional association networks: a new supervised approach based on regression trees
title_fullStr Prognostic transcriptional association networks: a new supervised approach based on regression trees
title_full_unstemmed Prognostic transcriptional association networks: a new supervised approach based on regression trees
title_short Prognostic transcriptional association networks: a new supervised approach based on regression trees
title_sort prognostic transcriptional association networks: a new supervised approach based on regression trees
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018815/
https://www.ncbi.nlm.nih.gov/pubmed/21098433
http://dx.doi.org/10.1093/bioinformatics/btq645
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