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This was implemented in MATLAB by reprocessing the data iteration by iteration; if data for the previous iteration had been present, the algorithm removed data with a 5% likelihood, and if data for the previous iteration had been absent, the algorithm removed data with a 25% likelihood.
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An important reason to remove data with Mendelian inconsistencies is that such errors in the data may reduce the power of subsequent analyses.
In order to reduce redundancy, results were therefore cleaned by removing data with identical eukaryotic kingdom combinations (e.g. human vs. yeast and mouse vs. yeast was reduced to one entry).
We further removed data points with Ka ≥ 0.05 and/or KS ≥ 0.05 from 523 human chimpanzee gene pairs to reduce the problem introduced by misalignment of coding sequences.
Since we focussed on how the biomarkers influenced ESCC prognosis in this study, we removed data for patients with missing data for more than eight missing biomarkers.
We removed data from spots with intensity levels lower than the median background level of 175 and log transformed and normalized data across arrays using the Lowess method.
In order to avoid constraining due to median or road edges, the authors have removed data obtained from carriageways with a width of less than three lanes.
This filtering was removing data vectors with current data residuals after inversion larger then constant thresholds, which can be set in the configuration for each component and data type.
An R2 of 98.4% was obtained by removing data points with expression values below background levels.
A further conservative analysis was also conducted by removing data for all participants with any missing data.
We implemented another sensitivity test by removing data for all children with a history of early chest illness, including croup, bronchitis, bronchiolitis, or pneumonia (Table 6).
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