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In all experiments, the internal control plasmid was used to compensate variable transfection efficiencies.
Another way to deal with different initial biomass concentrations is not to equalize, but to compensate variable growth kinetics during the main culture.
We employed Gaussian process classifiers (GPCs) as the primary classification approach in this study because they are fully probabilistic prediction devices and therefore provide predictions that can be easily adjusted to compensate for variable class priors (see section Accommodating Variable Class Priors).
In large-scale processes, standard PI (proportional-integral) or PID (proportional-integral-derivative) closed-loop control schemes are often adopted to compensate for variable disturbances and outliers.
To compensate for variable RNA and cDNA yields, the expression of HPRT was used as a control in mES experiments for which the optimal number of PCR cycles for linear amplification was determined.
Finally, we demonstrate how our approach can be used to compensate for variable class priors.
We demonstrated in this work that a simple numerical adjustment of these probabilistic predictions enables the classifier to easily compensate for variable class priors.
The concentration of filaggrin degradation products is normalized by the protein amount to compensate for variable amounts of SC harvested by tape stripping.
Our approach is also fully probabilistic, which means predictions can be easily corrected to compensate for variable class priors (e.g. disease prevalence).
In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications.
We compensated for variable sequencing depth between samples using the median-of-ratios method of DESeq2 (Love et al., 2014) version 1.6.3, and further performed a variance stabilizing transformation provided by the same package.
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