Granger causality vs. dynamic Bayesian network inference: a comparative study.
Granger causality vs. dynamic Bayesian network inference: a comparative study.
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DOI：
10.1186/1471-2105-10-122
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发表时间：
2009-04-24
影响因子：
3
通讯作者：
Feng J
中科院分区：
文献类型：
--
作者：
Zou C;Denby KJ;Feng J
在计算生物学中，人们经常面临的问题，推导不同元素之间的因果关系，如基因，蛋白质，代谢产物，神经元等，基于多维时间数据。目前，有两种常用的方法来探索元素之间的网络结构。一种是格兰杰因果关系方法，另一种是动态贝叶斯网络推理方法。这两种方法在文献中至少有几千篇出版物。一个关键问题是选择使用哪种方法来处理数据，特别是当它们产生相互矛盾的结果时。在本文中，我们提供了一个答案，专注于一个系统的和计算密集的比较两种方法的合成和实验数据。对于合成数据，发现了数据长度的临界点：当数据长度较短时，动态贝叶斯网络优于格兰杰因果关系方法，反之亦然。然后，我们在短长度的实验数据中测试我们的结果，这是当前生物实验中常见的情况：再次证实了动态贝叶斯网络的效果更好。当数据量较小时，动态贝叶斯网络推理的性能优于格兰杰因果关系方法;否则，格兰杰因果关系方法更好。
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better. When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.
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