Ranked prediction of p53 targets using hidden variable dynamic modeling

Barenco, Martino, Tomescu, Daniela, Brewer, Daniel ORCID: https://orcid.org/0000-0003-4753-9794, Callard, Robin, Stark, Jaroslav and Hubank, Michael (2006) Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biology, 7 (3). ISSN 1465-6906

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Abstract

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.

Item Type:	Article
Additional Information:	© 2006 Barenco et al.; licensee BioMed Central Ltd. 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.
Uncontrolled Keywords:	cell line, tumor,gamma rays,gene expression profiling,genes, p53,genetic variation,humans,models, genetic,models, theoretical,oligonucleotide array sequence analysis,precursor cell lymphoblastic leukemia-lymphoma,rna interference,transcription factors,transcription, genetic,tumor suppressor protein p53
Faculty \ School:	Faculty of Science > School of Biological Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Cancer Studies Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Depositing User:	Pure Connector
Date Deposited:	06 Jan 2014 14:16
Last Modified:	19 Oct 2023 01:15
URI:	https://ueaeprints.uea.ac.uk/id/eprint/47044
DOI:	10.1186/gb-2006-7-3-r25

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