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In this paper we present a method for modelling gene regulatory networks by forming a consensus Bayesian network model from multiple microarray gene expression datasets. Our method is based on combining Bayesian network graph topologies and does not require any special pre-processing of the datasets, such as re-normalisation. We evaluate our method on a synthetic regulatory network and part of the yeast heat-shock response regulatory network using publicly available yeast microarray datasets. Results are promising; the consensus networks formed provide a broader view of the potential underlying network, obtaining an increased true positive rate over networks constructed from a single data source.

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