Inferring the structure of gene regulatory networks from gene expression data has attracted a growing interest during the last years. Several machine learning related methods, such as Bayesian networks, have been proposed to deal with this challenging problem. However, in many cases, network reconstructions purely based on gene expression data do not lead to satisfactory results when comparing the obtained topology against a validation network.
Therefore, in this paper we propose an "inverse" approach: Starting from a priori specified network topologies, we identify those parts of the network which are relevant for the gene expression data at hand. For this purpose, we predict the expression level of a given gene from its relevant regulators.
Jochen Supper is a research assistant at the Centre for Bioinformatics (ZBIT), University of Tu"bingen. He is interested in deciphering regulatory signals controlling gene expression, analyzing the robustness of Signaling pathways, cluster multi-conditioned microarray datasets and immunoinformatics.
For more information please have a look at his web site: http://www-ra.informatik.uni-tuebingen.de/mitarb/supper/welcome_e.html
