Learning and assessing the robustness of Bayesian networks for biological data

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Bayesian Network models are an increasingly popular way to analyse biological data. In Biology application, Bayesian Networks could be viewed as a machine which produces outputs from inputs. Here the inputs are data, hyper-parameters and distribution, and the outputs are biological meaningful results like the possibility of variable expression and its links to phenotype. While studying the construction of Bayesian Networks has led to many insights for target gene discovery and drug development, such models are typically computing-expensive. The computation cost caused three main problems in Bayesian Network's application to biological data. Firstly, the sensitivity of Bayesian Networks' outputs to the changes in inputs is rarely accessed. Secondly, biologists normally expect a faster analysis method, which limits the application of Bayesian Network to first-hand data. Last but not least, there is a gap in optimising such models' feature to achieve a more biologically meaningful result. Optimisation requires experiment repetition and design in the inputs space. Therefore, a faster method to achieve Bayesian Networks' results is demanded. We propose to develop a robust Bayesian emulator to mimic the result from the Bayesian Network model efficiently, which could reduce the computation cost of Bayesian Networks. We will also explore novel biological references to validate the credibility of our model. Thus we could do experiment design over different inputs and optimise the performance of Bayesian Networks. The improved method would have potential in general biology analysis practice.