Learning and assessing the robustness of Bayesian networks for biological data

贝叶斯网络模型是分析生物数据的一种日益流行的方法。在生物学应用中，贝叶斯网络可以看作是一台从输入产生输出的机器。这里的输入是数据、超参数和分布，输出是生物学上有意义的结果，如变量表达的可能性及其与表型的联系。虽然研究贝叶斯网络的构建为靶基因发现和药物开发带来了许多见解，但此类模型通常计算昂贵。计算成本是贝叶斯网络应用于生物数据的三个主要问题。首先，贝叶斯网络的输出对输入变化的敏感度很少被研究。其次，生物学家通常期待更快的分析方法，这限制了贝叶斯网络对第一手数据的应用。最后但并非最不重要的一点是，在优化这类模型的功能以实现更具生物学意义的结果方面存在差距。优化需要在输入空间进行实验、重复和设计。因此，需要一种更快的方法来实现贝叶斯网络的结果。我们建议开发一个健壮的贝叶斯仿真器来高效地模拟贝叶斯网络模型的结果，从而降低贝叶斯网络的计算成本。我们还将探索新的生物学参考，以验证我们模型的可信度。因此，我们可以针对不同的输入进行实验设计，从而优化贝叶斯网络的性能。改进后的方法在一般生物分析实践中具有潜在的应用前景。

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.