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

Zou C, Denby KJ, Feng J - BMC Bioinformatics (2009)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Warwick, Coventry, UK. csrcbh@dcs.warwick.ac.uk

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.

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Related in: MedlinePlus

Granger causality and Bayesian network inference applied on insufficient number of data points for non-linear model. The grey edges in the inferred network structures indicate undetected causalities in our defined toy model. For each sample size n, we simulated a data set of 100 realizations of n time points. The Bayesian network structure represents a model average from these 100 realizations. High-confidence arcs, appearing in at least 95% of the networks are shown. The Granger causality inferred the structure according to the 95% confidence interval constructed by using the bootstrap method. (A) The sample size is 300. (B) The sample size is 150. (C) The sample size is 50.
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Figure 5: Granger causality and Bayesian network inference applied on insufficient number of data points for non-linear model. The grey edges in the inferred network structures indicate undetected causalities in our defined toy model. For each sample size n, we simulated a data set of 100 realizations of n time points. The Bayesian network structure represents a model average from these 100 realizations. High-confidence arcs, appearing in at least 95% of the networks are shown. The Granger causality inferred the structure according to the 95% confidence interval constructed by using the bootstrap method. (A) The sample size is 300. (B) The sample size is 150. (C) The sample size is 50.

Mentions: For a small sample size (see Figure 5), worse results are obtained for both approaches comparing to the previous linear model. Both approaches start to miss interactions when the sample size is smaller than 300. When the sample size is 150, the Bayesian network inference approach can detect one more true positive interaction than the Granger causality. However, when the sample size is 50, both approaches fail to detect all the interactions.


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

Zou C, Denby KJ, Feng J - BMC Bioinformatics (2009)

Granger causality and Bayesian network inference applied on insufficient number of data points for non-linear model. The grey edges in the inferred network structures indicate undetected causalities in our defined toy model. For each sample size n, we simulated a data set of 100 realizations of n time points. The Bayesian network structure represents a model average from these 100 realizations. High-confidence arcs, appearing in at least 95% of the networks are shown. The Granger causality inferred the structure according to the 95% confidence interval constructed by using the bootstrap method. (A) The sample size is 300. (B) The sample size is 150. (C) The sample size is 50.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691740&req=5

Figure 5: Granger causality and Bayesian network inference applied on insufficient number of data points for non-linear model. The grey edges in the inferred network structures indicate undetected causalities in our defined toy model. For each sample size n, we simulated a data set of 100 realizations of n time points. The Bayesian network structure represents a model average from these 100 realizations. High-confidence arcs, appearing in at least 95% of the networks are shown. The Granger causality inferred the structure according to the 95% confidence interval constructed by using the bootstrap method. (A) The sample size is 300. (B) The sample size is 150. (C) The sample size is 50.
Mentions: For a small sample size (see Figure 5), worse results are obtained for both approaches comparing to the previous linear model. Both approaches start to miss interactions when the sample size is smaller than 300. When the sample size is 150, the Bayesian network inference approach can detect one more true positive interaction than the Granger causality. However, when the sample size is 50, both approaches fail to detect all the interactions.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Warwick, Coventry, UK. csrcbh@dcs.warwick.ac.uk

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.

Show MeSH
Related in: MedlinePlus