论文标题
从稳态基因数据样本中推断概率布尔网络
Inferring probabilistic Boolean networks from steady-state gene data samples
论文作者
论文摘要
已经提出了概率布尔网络，用于估计动态系统的行为，因为它们将基于规则的建模与不确定性原理相结合。但是，直接从基因数据中推断PBN是具有挑战性的，尤其是当数据收集和/或嘈杂（例如，在基因表达谱数据的情况下）时。在本文中，我们提出了一种可再现的方法，可以直接从系统处于稳态时进行的真实基因表达数据测量值来推断PBN。 PBN的稳态动力学在生物机械分析中具有特别的兴趣。所提出的方法不依赖于重建网络的状态演变，这在较大的网络上在计算上很棘手。我们证明了来自著名的转移性黑色素瘤研究的真实基因表达谱图样品的方法。该管道是使用Python实施的，我们可以公开使用。
Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available.
