Reconstruction of gene regulatory networks from postgenomic data
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Date
2007Author
Werhli, Adriano Velasque
Metadata
Abstract
An important problem in systems biology is the inference of biochemical pathways
and regulatory networks from postgenomic data. The recent substantial increase
in the availability of such data has stimulated the interest in inferring the networks
and pathways from the data themselves. The main interests of this thesis
are the application, evaluation and the improvement of machine learning methods
applied to the reverse engineering of biochemical pathways and networks. The
thesis starts with the application of an established method to newly available gene
expression data related to the interferon pathway of the human immune system
in order to identify active subpathways under di erent experimental conditions.
The thesis continues with the comparative evaluation of various machine learning
methods (Relevance networks, Graphical Gaussian Models, Bayesian networks)
using observational and interventional data from cytometry experiments as well
as simulated data from a gold-standard network. The thesis also extends and improves
existing methods to include biological prior knowledge under the Bayesian
approach in order to increase the accuracy of the predicted networks and it quanti
es to what extent the reconstruction accuracy can be improved in this way.