Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

@inproceedings{Zou2009IdentifyingII,
  title={Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach},
  author={Cunlu Zou and Christophe Ladroue and Shuixia Guo and Jianfeng Feng},
  booktitle={BMC Bioinformatics},
  year={2009}
}
BackgroundReverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 28 REFERENCES

Dynamic proteomics of individual cancer cells in response to a drug.

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Systems Biology Strikes Gold

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Beyond element-wise interactions: Defining group-to-group interactions for biological processes

  • C Ladroue, SX Guo, KM Kendrick, JF Feng
  • PLoS One 2009,
  • 2009
VIEW 1 EXCERPT

The Fourth Way: Granger Causality is better than the three other Reverse-engineering Approaches

  • CL Zou, KM Kendrick, JF Feng
  • COMMENTS ON Cell
  • 2009
VIEW 1 EXCERPT

Kernel-Granger causality and the analysis of dynamical networks.

VIEW 1 EXCERPT