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Báo cáo y học: "Ranked prediction of p53 targets using hidden variable dynamic modeling"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Ranked prediction of p53 targets using hidden variable dynamic modeling. | Method Open Access Ranked prediction of p53 targets using hidden variable dynamic modeling Martino Barenco Daniela Tomescu Daniel Brewer Robin Callard Jaroslav Stark and Michael Hubank Addresses Institute of Child Health University College London Guilford Street London WC1N 1EH UK. Complex Centre for Mathematics and Physics in the Life Sciences and Experimental Biology University College London Stephenson Way London NW1 2HE UK. Department of Mathematics Imperial College London London SW7 2AZ UK. Correspondence Michael Hubank. Email m.hubank@ich.ucl.ac.uk Published 31 March 2006 Genome Biology 2006 7 R25 doi 10.1 186 gb-2006-7-3-r25 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2006 7 3 R25 Received 24 November 2005 Revised 30 January 2006 Accepted 21 February 2006 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. 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. Background In order to understand how gene networks function it is necessary to identify their components and to quantitatively describe how they relate to one another 1-3 . Subsequent prediction of gene network behavior requires identification of important parameters and variables and .