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Power was estimated by generating simulated data, then using the simulated data to try to estimate the original model parameters.
In some instances, validation was performed by generating simulated data so as to create a set of known true positives (TP) and true negatives (TN) [ 8– 10].
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We will also introduce a new graphical tool for aiding experimental design by surveying the concentration space and generating simulated data sets, which can be subsequently statistically examined for their information content.
By use of the same forward solver for generating simulated data and for solving inverse problem, the reconstruction can be fairly successful [ 28].
MJN designed the method for generating simulated data.
Based on the simulation engine developed by Morris et al. (2005) and code (R and MATLAB) to generate simulated data proposed by Cruz-Marcelo et al. (2008) and Zhang et al. (submitted for publication), we also create two new simulated datasets to investigate noise affection on different algorithms.
We also generated simulated data from the second, nonlinear, model.
We generated simulated data under different levels of noise and evaluated the performance of our RMN algorithm by using the above mentioned four evaluation measures.
We generate simulated data based on the data of Ding et al. (2008).
Our method employs Genetic Algorithm to search for the most likely network topologies by iteratively generating simulated protein phosphorylation data using ODEs and the rewired network and then fitting the simulated data with real data of cancer signalling and cell fate.
Authors generated simulated expression data, including biological noise, by using SynTReN.
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