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The equation was an approximation to the cellular response and was justified only by qualitative data, thereby making it a potential source of error.
The machine learning technique objective is to find the best available approximation to the supervisor's response.
In the next several sections, we apply the multiscale approximation to the heat shock response model of E. coli and derive the limiting models.
Since Equation 1 is an approximation to the true Fano factor, it is important to assess the range over which it is valid.
Upon numerical solution of this equation, analytical approximations to the response moments can be obtained.
Unfortunately even obtaining a constant factor approximation to the n-clustering problem is NP-hard (Sahni and Gonzalez, 1976).
Subsequently, we propose a greedy algorithm that is shown to be a constant-factor approximation to the optimal deployment.
This model can be used as an approximation to the true model in cases of heterogeneity of response in complex biological systems.
Generations were converted to dS using a factor of 10−4, an approximation to the mutation rate used in the simulation.
Convergence of the finite cloud approximation to the tabulated semi-infinite cloud values for dose conversion factors was validated.
The analytical model used simulates an approximation to the more widespread socio-structural and environmental conditions that influence the response variable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com