Learning Latent Dynamic Bayesian Networks from High Dimensional InterventionEffects and Applications in Systems Biology

因果动态贝叶斯网络（DBNs）的结构学习在许多应用领域，包括医学和系统生物学。因此，已经提出了不同的方法来从介入数据学习DBN。到目前为止，很少有人注意到这样的情况，即感兴趣的网络不能直接观察到，而只能通过它对一层可观察变量的影响来观察。这些模型的需求出现在系统生物学中，蛋白质信号网络往往不能直接测量，但数以千计的分子物种的扰动，如基因敲除，可以廉价recorded.The项目的目的是建立一个通用的框架，从静态和时间序列扰动数据的结构学习隐藏DBNs，这是实际适用于这种情况。我们的目标将需要一个现实的隐变量网络中的信号传播的概率建模。此外，我们将研究干预计划，占不确定性和同时瞄准多个隐藏变量。一个主要的任务是为我们的模型开发和实现一个有效的结构学习算法，它允许应用到现实大小的数据集，例如，在系统biology.We将验证我们的方法在广泛的模拟和使用它的结构学习的生物网络从实验数据。我们希望我们的方法将显着提高此类网络的自动重建，从而提高对因果关系的理解。我们的工作也可能影响其他科学领域，其中不可观察变量的因果推理是相关的，例如心理学或社会学。

Structure learning of causal Dynamic Bayesian Networks (DBNs) is relevant in many application domains, including medicine and systems biology. Accordingly, different methods have been proposed to learn DBNs from interventional data. So far little attention has been paid to situations in which the network of interest cannot be observed directly, but only through its effects on a layer of observable variables. The need for these models arises in systems biology, where protein signaling networks can often not be measured directly, but the response of thousands of molecular species to perturbations, like gene knock-downs, can be recorded cheaply.The aim of this project is to establish a general framework for structure learning of hidden DBNs from static and time series perturbation data, which is practically applicable to this situation. Our goal will require a realistic probabilistic modeling of signal propagation in a network of hidden variables. Moreover, we will investigate intervention schemes which account for uncertainty and simultaneous targeting of multiple hidden variables. A major task is the development and implementation of an efficient structure learning algorithm for our model, which allows for application to data sets of realistic size that appear, for instance, in systems biology.We will validate our approach in extensive simulations and use it for structure learning of biological networks from experimental data. We expect that our method will significantly improve the automated reconstruction of such networks and thus improve their causal understanding. Our work might impact other scientific domains as well, in which causal inference of unobservable variables is of relevance, e.g. psychology or sociology.