Publication: Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
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Marbach, D., Schaffter, T., Mattiussi, C., & Floreano, D. (2009). Generating realistic in silico gene networks for performance assessment of reverse engineering methods. Journal of Computational Biology, 16(2), 229–239. https://doi.org/10.1089/cmb.2008.09TT
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Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper, we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extrac
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- Marbach, Daniel
- Schaffter, Thomas
- Mattiussi, Claudio
- Floreano, Dario
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Marbach, D., Schaffter, T., Mattiussi, C., & Floreano, D. (2009). Generating realistic in silico gene networks for performance assessment of reverse engineering methods. Journal of Computational Biology, 16(2), 229–239. https://doi.org/10.1089/cmb.2008.09TT
