Analysis of microarray data to infer transcription regulation in the yeast cell cycle

The experimental microarray data has the potential application in determining the underlying mechanisms of transcription regulation in a living cell. The inference of this regulation circuitry with computational methods suffers from two major challenges: the low accuracy of inferring true positive connections and the excessive computation time. In this paper, we show that models based on Dynamic Bayesian Networks which exploit the biological features of gene expression are more computationally efficient and topologically accurate compared to the other existing models. Using two experimental microarray datasets of the yeast cell cycle, we also evaluate how successfully the available models can address the current challenges with the increasing size of the datasets.