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» Bio-inspired reverse engineering of regulatory networks
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BMCBI
2010
172views more  BMCBI 2010»
13 years 11 months ago
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Alina Sîrbu, Heather J. Ruskin, Martin Crane
BMCBI
2008
166views more  BMCBI 2008»
13 years 11 months ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
TCS
2011
13 years 5 months ago
An algorithmic framework for network reconstruction
Models of biological systems and phenomena are of high scientific interest and practical relevance, but not always easy to obtain due to their inherent complexity. To gain the re...
Markus Durzinsky, Annegret Wagler, Robert Weismant...
GECCO
2006
Springer
202views Optimization» more  GECCO 2006»
14 years 2 months ago
Inference of genetic networks using S-system: information criteria for model selection
In this paper we present an evolutionary approach for inferring the structure and dynamics in gene circuits from observed expression kinetics. For representing the regulatory inte...
Nasimul Noman, Hitoshi Iba
BMCBI
2007
168views more  BMCBI 2007»
13 years 10 months ago
Bayesian model-based inference of transcription factor activity
Background: In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is co...
Simon Rogers, Raya Khanin, Mark Girolami