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

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Zou, Cunlu, Denby, Katherine J. and Feng, Jianfeng. (2009) Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics, Vol.10 . Article number 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 > Computer Science
Faculty of Science > Life Sciences (2010- )
Faculty of Science > Centre for Scientific Computing

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

Official Date:

24 April 2009

Dates:

Date	Event
24 April 2009	Published

Volume:

Vol.10

Article Number:

Article number 122

Identification Number:

10.1186/1471-2105-10-122

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Open Access

Funder:

Engineering and Physical Sciences Research Council (EPSRC), Seventh Framework Programme (European Commission) (FP7)

Grant number:

EP/E002331/1 (EPSRC)

Related URLs:

Publisher

References:

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