Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.

中文翻译：

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评估因果推断方法对基因调控网络的有效性。

因果关系推断是使用计算技术来预测一组变量的可能因果关系，从而形成有向网络。由于可用数据的限制以及现有因果推论技术的效率不足，基因调控网络（GRN）中的因果推论是一项重要而又具有挑战性的任务。已经提出了许多技术，并将其应用于各种领域中的因果关系推断，尽管它们并非特定于监管网络推断。在本文中，我们评估了推断因果关系的方法的有效性。我们介绍了七种不同的推理方法，并将它们应用于GRN中的有向边推理。我们使用来自DREAM挑战的时间序列表达数据来评估推理方法的质量，并根据性能对其进行排名。最佳方法应用于乳腺癌数据以推断因果关系网络。实验结果表明，因果熵是最好的，但是，它非常耗时并且在相对较大的网络中不可行。我们用第二好的方法，即转移熵来推断乳腺癌的GRN。该网络的拓扑分析表明，最高水平的基因（例如SLC39A5）被认为是中心基因，在癌症进展中起重要作用。