Identifying gene regulatory networks using Bayesian networks and domain knowledge

Bayesian network techniques have been used for discovering causal relationships among large number of variables in many applications. This thesis demonstrates how Bayesian techniques are used to build gene regulation networks. The contribution of this thesis is to find a novel way of combining pre-knowledge (biological domain information) into Bayesian network learning process for microarray data analysis. Such pre-knowledge includes biological process, cellular component and molecular function information and cell cycle information. Incorporating preexisting knowledge into the Bayesian network learning process significantly improves the accuracy and performance of learning. Another contribution of this thesis is the inference and validation of learning result based on the biological literature and biological knowledge. The learned network structure is presented graphically to make the results easy to understand. A yeast microarray dataset is used to test the performance of the learning process.