Ranked prediction of p53 targets using hidden variable dynamic modeling

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generat...

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Detalles Bibliográficos
Autores principales: Barenco, Martino, Tomescu, Daniela, Brewer, Daniel, Callard, Robin, Stark, Jaroslav, Hubank, Michael
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557743/
https://www.ncbi.nlm.nih.gov/pubmed/16584535
http://dx.doi.org/10.1186/gb-2006-7-3-r25
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author Barenco, Martino
Tomescu, Daniela
Brewer, Daniel
Callard, Robin
Stark, Jaroslav
Hubank, Michael
author_facet Barenco, Martino
Tomescu, Daniela
Brewer, Daniel
Callard, Robin
Stark, Jaroslav
Hubank, Michael
author_sort Barenco, Martino
collection PubMed
description Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.
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spelling pubmed-15577432006-09-01 Ranked prediction of p53 targets using hidden variable dynamic modeling Barenco, Martino Tomescu, Daniela Brewer, Daniel Callard, Robin Stark, Jaroslav Hubank, Michael Genome Biol Method Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively. BioMed Central 2006 2006-03-31 /pmc/articles/PMC1557743/ /pubmed/16584535 http://dx.doi.org/10.1186/gb-2006-7-3-r25 Text en Copyright © 2006 Barenco et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Barenco, Martino
Tomescu, Daniela
Brewer, Daniel
Callard, Robin
Stark, Jaroslav
Hubank, Michael
Ranked prediction of p53 targets using hidden variable dynamic modeling
title Ranked prediction of p53 targets using hidden variable dynamic modeling
title_full Ranked prediction of p53 targets using hidden variable dynamic modeling
title_fullStr Ranked prediction of p53 targets using hidden variable dynamic modeling
title_full_unstemmed Ranked prediction of p53 targets using hidden variable dynamic modeling
title_short Ranked prediction of p53 targets using hidden variable dynamic modeling
title_sort ranked prediction of p53 targets using hidden variable dynamic modeling
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557743/
https://www.ncbi.nlm.nih.gov/pubmed/16584535
http://dx.doi.org/10.1186/gb-2006-7-3-r25
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