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Granger causality vs. dynamic Bayesian network inference: a comparative study

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Zou, Cunlu and Feng, Jianfeng. (2009) Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics, Vol.10 (No.122). ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/1471-2105-10-122

Abstract

Background 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. 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. Conclusion 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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Bioinformatics, Bayesian statistical decision theory, Biology -- Data processing, Structural bioinformatics
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Date:	24 April 2009
Volume:	Vol.10
Number:	No.122
Identification Number:	10.1186/1471-2105-10-122
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Seventh Framework Programme (European Commission) (FP7/2007-2013)
Grant number:	EP/E002331/1 (EPSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/796