Ranked prediction of p53 targets using hidden variable dynamic modeling.

Martino Barenco; Daniela Tomescu; Daniel Brewer; Robin Callard; Jaroslav Stark; Michael Hubank

doi:10.1186/gb-2006-7-3-r25

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Ranked prediction of p53 targets using hidden variable dynamic modeling.

Martino Barenco·Daniela Tomescu·Daniel Brewer·Robin Callard·Jaroslav Stark·Michael Hubank

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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.

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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 Journal Article Research Support, Non-U.S. Gov't

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Martino Barenco, Daniela Tomescu, Daniel Brewer, Robin Callard, Jaroslav Stark, Michael Hubank. 2006-03-31. Ranked prediction of p53 targets using hidden variable dynamic modeling.. https://doi.org/10.1186/gb-2006-7-3-r25

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