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

Ranked prediction of p53 targets using hidden variable dynamic modeling

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

Research output: Contribution to journal › Article › peer-review

115 Citations (Scopus)

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.

Original language	English
Article number	R25
Journal	Genome Biology
Volume	7
Issue number	3
DOIs	https://doi.org/10.1186/gb-2006-7-3-r25
Publication status	Published - 31 Mar 2006

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

Access to Document

10.1186/gb-2006-7-3-r25Licence: CC BY

Cite this

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title = "Ranked prediction of p53 targets using hidden variable dynamic modeling",

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.",

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",

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AU - Barenco, Martino

AU - Tomescu, Daniela

AU - Brewer, Daniel

AU - Callard, Robin

AU - Stark, Jaroslav

AU - Hubank, Michael

N1 - © 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.

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Y1 - 2006/3/31

N2 - 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.

AB - 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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KW - Gamma Rays

KW - Gene Expression Profiling

KW - Genes, p53

KW - Genetic Variation

KW - Humans

KW - Models, Genetic

KW - Models, Theoretical

KW - Oligonucleotide Array Sequence Analysis

KW - Precursor Cell Lymphoblastic Leukemia-Lymphoma

KW - RNA Interference

KW - Transcription Factors

KW - Transcription, Genetic

KW - Tumor Suppressor Protein p53

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