Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.

中文翻译：

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通过概率编程推断信号通路

细胞通过令人眼花缭乱的复杂生化过程（称为信号传导途径）进行自我调节。这些通常被描述为一个网络，其中节点代表蛋白质，边缘表示它们对彼此的影响。为了在细胞水平上了解疾病和治疗方法，准确了解起作用的信号通路至关重要。由于信号传导途径可能会因疾病而改变，因此从病情或患者特定数据推断信号传导途径的能力非常有价值。存在多种用于推断信号传导途径的技术。我们以过去的工作为基础，将信号通路推断制定为磷酸化蛋白质组时间过程数据的动态贝叶斯网络结构估计问题。我们采用贝叶斯方法，使用马尔可夫链蒙特卡罗来估计可能的动态贝叶斯网络结构的后验分布。我们的主要贡献是（i）一种新颖的提案分布，可以有效地对稀疏图进行采样，以及（ii）放宽常见的限制性建模假设。