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An algorithm for score aggregation over causal biological networks based on random walk sampling

机译：基于随机游动抽样的因果生物网络得分聚合算法

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BackgroundWe recently published in BMC Systems Biology an approach for calculating the perturbation amplitudes of causal network models by integrating gene differential expression data. This approach relies on the process of score aggregation, which combines the perturbations at the level of the individual network nodes into a global measure that quantifies the perturbation of the network as a whole. Such "bottom-up" aggregation relates the changes in molecular entities measured by omics technologies to systems-level phenotypes. However, the aggregation method we used is limited to a specific class of causal network models called "causally consistent", which is equivalent to the notion of balance of a signed graph used in graph theory. As a consequence of this limitation, our aggregation method cannot be used in the many relevant cases involving "causally inconsistent" network models such as those containing negative feedbacks.
机译：背景我们最近在BMC Systems Biology中发布了一种通过整合基因差异表达数据来计算因果网络模型的摄动幅度的方法。这种方法依赖于分数汇总的过程，该过程将单个网络节点级别的扰动组合为一个整体度量，该度量对整个网络的扰动进行量化。这种“自下而上”的聚集将通过组学技术测得的分子实体的变化与系统级表型联系起来。但是，我们使用的聚合方法仅限于一类特定的因果网络模型，称为“因果一致”，这等效于图论中使用的带符号图的平衡概念。由于这种限制，我们的汇总方法不能用于涉及“因果不一致”的网络模型的许多相关情况，例如包含负反馈的网络模型。

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