Inferring Signalling Networks from Longitudinal Data
Author Information
Author(s): Bender Christian, Heyde Silvia, Henjes Frauke, Wiemann Stefan, Korf Ulrike, Beißbarth Tim
Primary Institution: German Cancer Research Center (DKFZ)
Hypothesis
Can we reconstruct biological networks from longitudinal high-throughput data using sampling-based approaches?
Conclusion
The R-package 'ddepn' allows for effective network inference from longitudinal high-throughput data using two different sampling-based algorithms.
Supporting Evidence
- The package 'ddepn' is freely available on R-Forge and CRAN.
- It implements a Markov Chain Monte Carlo method for sampling network structures.
- The study shows how prior knowledge can improve network inference results.
Takeaway
This study introduces a tool that helps scientists understand how proteins interact over time, especially in cancer research.
Methodology
The study presents an R-package that implements a Markov Chain Monte Carlo method and a Genetic Algorithm for network reconstruction from longitudinal data.
Potential Biases
The algorithms may be biased towards prior knowledge if the prior influence is set too strong.
Limitations
The performance of the algorithms can vary based on the prior knowledge included and the settings of the parameters.
Digital Object Identifier (DOI)
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