标题： 贝叶斯方法推断基因调控网络中局部因果结构的研究 显示英文标题

标题： A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks

摘要： 基因调控网络在控制生物体的生物过程中起着至关重要的作用，这就是为什么人们非常感兴趣开发能够从高通量遗传数据中提取其结构的计算方法。 一种典型的方法包括对协方差结构的一系列条件独立性检验，旨在逐步缩小可能因果模型的空间。 我们提出了一种新的有效的贝叶斯方法，用于发现三变量（通常为正态分布）之间的局部因果关系。 在我们的方法中，我们一次性对协方差矩阵中的模式进行评分，并以因果结构的先验形式纳入可用的背景知识。 我们的方法具有灵活性，因为它允许不同类型的因果结构和假设。 我们将这种方法应用于通过学习基因表达水平之间的调控关系来推断基因调控网络的任务。 我们展示了我们的算法能够产生稳定且保守的局部因果结构后验概率估计，这些估计可以用来推导出最具有意义的调控关系的诚实排名。 我们在模拟数据和酵母实验的真实世界数据上都证明了我们方法的稳定性和有效性。 显示英文摘要

摘要： Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence tests on the covariance structure meant to progressively reduce the space of possible causal models. We propose a novel efficient Bayesian method for discovering the local causal relationships among triplets of (normally distributed) variables. In our approach, we score the patterns in the covariance matrix in one go and we incorporate the available background knowledge in the form of priors over causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. We apply the approach to the task of inferring gene regulatory networks by learning regulatory relationships between gene expression levels. We show that our algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. We demonstrate the stability and efficacy of our method both on simulated data and on real-world data from an experiment on yeast.