Inference of biological networks using Bi-directional Random Forest Granger causality.
Inference of biological networks using Bi-directional Random Forest Granger causality.
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DOI：
10.1186/s40064-016-2156-y
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Siyal MY
中科院分区：
文献类型：
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作者：
Furqan MS;Siyal MY
基于标准最小二乘的格兰杰因果关系是一种广泛使用的检测时间序列数据之间因果关系的方法。然而，由于高维数据的可用性，技术的最新发展限制了一些现有实现的利用。在本文中，我们提出了一种技术称为双向随机森林格兰杰因果关系。该技术使用随机森林正则化以及通过反转时间戳来重用时间序列数据以提取更多因果信息的想法。我们已经证明了我们提出的方法的有效性，通过将其应用于模拟数据，然后将其应用于两个真实的生物数据集，即，fMRI和HeLa细胞。fMRI数据用于绘制涉及演绎推理的脑网络，而HeLa细胞数据集用于绘制涉及癌症的基因网络。
The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.