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For example, to deal with noise, Boudraa and Cexus[7] removed the high-frequency components using a filtering method, and Wu and Huang[8] used the ensemble mean approach of the simulated signal.
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In Section 4, we evaluated the performance of our proposed approach on the simulated data and presented the applications of the proposed methods to an analysis of lung cancer data.
We demonstrate the effectiveness of the proposed approach on the simulated and experimental data.
Here we sketch an approach combining Markov models of the simulated dynamics with dynamical fingerprints to link between simulation and experiment.
In the direct comparisons approach the 500 simulated data closest to the observed values were used, while in the regression approach we used 1% of the simulated data closest to the observed data set.
In this study, the performance of the MGD method was better than that of the Bayesian approaches in the simulated scenarios with a higher number of clusters and a more complex population structure.
The approach is validated by comparison of the simulated results with the experimental results reported in the literature.
Then, similar to the g-formula, we simulated a cohort using those distributions; however, while the g-formula would also incorporate the treatment regimen of interest, our approach incorporated this after expansion of the simulated cohort by censoring individuals when no longer compliant.
A good similarity was discovered in the findings by the two approaches, which suggested the usefulness of the simulated environment method.
Third, we find that using random negatives during training improves the performance of the machine learning approaches in the simulated target-prediction experiments.
This approach made possible the direct comparison of the simulated cellular phenotypes described by P in the model and the level of CD56 protein that describes the phenotype of the living cells.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com