In using gene expression levels for genetic network inference, we believe that two measurements that are similar to each other are less informative than two measurements that differ from each other. Given, for example, that gene expression levels measured at two adjacent time points in a time-series experiment are often similar to each other, we assume that each measurement in the time-series experiment will be less informative than each measurement in a steady-state experiment. Based on this idea, we propose a new inference method that relies heavily on informative gene expression data. Through numerical experiments, we prove that the quality of an inferred genetic network is slightly improved by heavily weighting informative gene expression data. In this study, we develop a new method by modifying the existing random-forest-based inference method to take advantage of its ability to analyze both time-series and static gene expression data. The idea we propose can be similarly applied to many of the other existing inference methods, as well.

中文翻译：

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使用随机森林推断遗传网络：为基因表达数据分配不同的权重

在使用基因表达水平进行遗传网络推断时，我们认为彼此相似的两个测量值比彼此不同的两个测量值信息量少。例如，考虑到在时间序列实验中两个相邻时间点测量的基因表达水平通常彼此相似，我们假设时间序列实验中的每个测量值将比稳态中的每个测量值提供更少的信息。状态实验。基于这个想法，我们提出了一种新的推理方法，该方法严重依赖于信息丰富的基因表达数据。通过数值实验，我们证明通过大量加权信息基因表达数据，推断的遗传网络的质量略有提高。在这项研究中，我们通过修改现有的基于随机森林的推理方法来开发一种新方法，以利用其分析时间序列和静态基因表达数据的能力。我们提出的想法也可以类似地应用于许多其他现有的推理方法。