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This approach compensates for DNA/chromatin staining intensity variations between individual cells, cell types and experiments.
This approach compensates for differences in target and reference gene amplification efficiency within an experiment and between experiments.
This approach compensates for any hardware influence, such as RF-profile or eddy currents, but will fail if the tissue water content is altered.
This approach compensates for residual variability resulting from spatial variation in parameters that were not included in the models, such as land use, urbanization, air-conditioning use, population density, and water storage practices (Hu et al. 2010c).
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The consensus approach compensates for this to some degree, allowing each program to reaffirm the predictions made by the others, and improving confidence by removing many of the false predictions generated by data artifacts.
Although the multiscale Finite Element Analysis (FEA) of the real microstructures encounters with some difficulties, such as computational costs, the micromechanics approach compensate for the FEA limitations.
This difference-based approach compensates for random cell culturing artefacts and should identify the regions most strongly linked to clinical disease.
This approach also compensates for any theoretical over-representation of proteins in pooled samples by a single or small number of individuals.
This approach also compensates for spatial variation of residuals resulting from other factors with spatial variation, e.g., population density, home address and psychiatric healthcare facilities, which have also been applied in studies in other diseases [ 26, 51].
However, the stratified and multivariable approaches compensate for this problem to some degree, making possible estimation of odds ratios (ORs) and percentages relevant to subgroups.
Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters.
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