ArticleBMC bioinformatics2010
Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Detecting existence of a hidden mediator between a pair of individual time series.Scientific reports · 2025Article
- Description length guided nonlinear unified Granger causality analysis.Network neuroscience (Cambridge, Mass.) · 2023Article
- Temporal signals drive the emergence of multicellular information networks.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Information Flow Network of International Exchange Rates and Influence of Currencies.Entropy (Basel, Switzerland) · 2021Article
- A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience.Neuroinformatics · 2016Article
- Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue.Journal of neuroscience methods · 2015Article
- Granger causality-based synaptic weights estimation for analyzing neuronal networks.Journal of computational neuroscience · 2015Article
- An algebra-based method for inferring gene regulatory networks.BMC systems biology · 2014Article
- Systems biology data analysis methodology in pharmacogenomics.Pharmacogenomics · 2011Review
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4 authors.
Funding
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Abstract
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 equations (ODE), Bayesian networks, information theory and Granger Causality.
resultsHere we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
conclusionsThe results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.
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