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Ranked prediction of p53 targets using hidden variable dynamic modeling

Martino Barenco12, Daniela Tomescu1, Daniel Brewer12, Robin Callard12, Jaroslav Stark23 and Michael Hubank12*

Author Affiliations

1 Institute of Child Health, University College London, Guilford Street, London WC1N 1EH, UK

2 CoMPLEX (Centre for Mathematics and Physics in the Life Sciences and Experimental Biology), University College London, Stephenson Way, London, NW1 2HE, UK

3 Department of Mathematics, Imperial College London, London SW7 2AZ, UK

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Genome Biology 2006, 7:R25  doi:10.1186/gb-2006-7-3-r25

Published: 31 March 2006


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.