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From network N1, we will infer the rewired network N2 corresponding to cancer cell lines by fitting simulated data to the cancer specific dataset.
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This finding is demonstrated by fitting the 3D simulated data on the short timescale, to the analytical expressions for 2D diffusion and/or exchange (Figure 3B).
For each bootstrap replication, fitting the simulated data along the LF family determines ζ and thus the simulated value ERR =.
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.
Based on the results of fitting the simulated data, we demonstrated accurate extraction of optical properties of two-layered media.
We observe that these results approximately fits the simulated data.
Both model A and Model B fit the simulated data, but considerably underestimate n0, compared to model C [22].
We then fit these simulated data with the "full" model that was applied to our empirical data.
Four) The model is fitted to the simulated data with allowed to vary freely within the parameter space, yielding new fitted values for each cell.
Cox models were fitted to the simulated data, with between 1 and 29 uniformly distributed covariates.
Figure 13 Comparison of the exponential fit to the simulated data and the polynomial fit.
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