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

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Correction After publication of this work [1], we noted that we inadvertently failed to include the complete list of all coauthors. The full list of authors has now been added and the Authors' contributions and Acknowledgements section modified accordingly. Authors' contributions The whole work was carried out by CZ and supervised by JF. The experimental data was provided by KD.

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

Cunlu Zou, Jianfeng Feng (2009)

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.

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Journal

Journal ID (nlm-ta): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central

ISSN (Electronic): 1471-2105

Publication date Collection: 2009

Publication date (Electronic): 7 December 2009

Volume: 10

Page: 401

Affiliations

[1 ]Department of Computer Science, University of Warwick, Coventry, UK

[2 ]Warwick HRI and Warwick Systems Biology Centre, Warwick, UK

[3 ]Centre for Computational Systems Biology Fudan University, Shanghai, PR China

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Publisher ID: 1471-2105-10-401

DOI: 10.1186/1471-2105-10-401

PMC ID: 2795767

SO-VID: 342020b8-2381-44be-a92d-102418cd7486

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This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

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

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