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It takes time for the model to adjust to the new environment.
To assess the effects of doxycycline treatment we used a mixed effects model to adjust for treatment and genotype and their interaction, with adjustment for correlation within mouse.
We used Cox proportional hazards model to adjust for confounders related to survival at 3 years.
Moreover, we build a traffic-strength- and network-density-based model to adjust essential algorithm parameters adaptively.
We then propose a correction model to adjust for observed differences in terms of age and schooling.
Concretely, we first fine-tune the pre-trained CNN model to adjust the parameters for visual cloud information.
We also included an intragroup correlation in the model to adjust for multiple admissions by the same patient.
Age class and sex were incorporated in the model to adjust for joint confounding.
Finally, we developed a simple computational model to adjust the experimental force spectra.
Ultimately, it would be very useful to incorporate LSI-derived literature similarities, perhaps as priors in a Bayesian model, to adjust the FDR for microarray datasets.
The data analysis was completed using the restricted/residual maximum likelihood-based mixed-effect model to adjust the intracorrelation effect for the mice that had multiple measurements.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com