Ranked prediction of p53 targets using hidden variable dynamic modeling
Open Access
- 31 March 2006
- journal article
- Published by Springer Nature in Genome Biology
- Vol. 7 (3) , R25
- https://doi.org/10.1186/gb-2006-7-3-r25
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
This publication has 46 references indexed in Scilit:
- Quantitative inference of dynamic regulatory pathways via microarray dataBMC Bioinformatics, 2005
- Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarraysNature Genetics, 2004
- Regulation of Gene Expression by a Metabolic EnzymeScience, 2004
- Transcriptional regulatory code of a eukaryotic genomeNature, 2004
- Quantitative characterization of the transcriptional regulatory network in the yeast cell cycleBioinformatics, 2004
- Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite dataBioinformatics, 2004
- Reconstructing gene networks: what are the limits?Biochemical Society Transactions, 2003
- Inferring Genetic Networks and Identifying Compound Mode of Action via Expression ProfilingScience, 2003
- Transcriptional Regulatory Networks in Saccharomyces cerevisiaeScience, 2002
- Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kineticsProceedings of the National Academy of Sciences, 2002