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» Estimating dynamic models for gene regulation networks
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BMCBI
2007
164views more  BMCBI 2007»
13 years 5 months ago
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...
ECCB
2003
IEEE
13 years 10 months ago
Gene networks inference using dynamic Bayesian networks
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactio...
Bruno-Edouard Perrin, Liva Ralaivola, Aurél...
ICASSP
2011
IEEE
12 years 9 months ago
Uncover cooperative gene regulations by microRNAs and transcription factors in glioblastoma using a nonnegative hybrid factor mo
—Transcriptional regulation by transcription factors (TFs) and microRNAs controls when and how much RNA is created. Due to technical limitations, the protein level expressions of...
Jia Meng, Hung-I Harry Chen, Jianqiu Zhang, Yidong...
ISMB
2004
13 years 6 months ago
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. First we assess the ability of currently available gene regulatory ne...
Michiel J. L. de Hoon, Yuko Makita, Seiya Imoto, K...
BIOINFORMATICS
2006
118views more  BIOINFORMATICS 2006»
13 years 5 months ago
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
Guido Sanguinetti, Magnus Rattray, Neil D. Lawrenc...