Ranked prediction of p53 targets using hidden variable dynamic modeling

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Ranked prediction of p53 targets using hidden variable dynamic modeling

Barenco, M; Tomescu, D; Brewer, D; Callard, R; Stark, J; Hubank, M; (2006) Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biology , 7 , Article R25. 10.1186/gb-2006-7-3-r25. Green open access

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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.

Type:	Article
Title:	Ranked prediction of p53 targets using hidden variable dynamic modeling
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1186/gb-2006-7-3-r25
Publisher version:	http://dx.doi.org/10.1186/gb-2006-7-3-r25
Language:	English
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.
Keywords:	art. no. 17, gene-expression, dna-damage, microarray data, lymphoblastoid-cells, regulatory networks, ionizing-radiation, tumor-suppressor, binding-sites, responses
UCL classification:	UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI:	https://discovery.ucl.ac.uk/id/eprint/1335292