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The Naive Bayes classifier has not needed any misclassification costs for 90% of the datasets, however in 60% of the datasets there are greater than 20% False Positives.
Between the datasets, however, differing allele frequencies are exhibited as indicated by clustering in the principal components analysis of the combined data.
A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow.
To reduce the amount of noise in the datasets, however, we used data smoothing with the T4253H algorithm, 17 thus ensuring easy interpretation of the figures illustrating the patterns of change.
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Given the size of the dataset however, manually checking and correcting the entire dataset appeared to be impractical.
The presence of error in the dataset, however, does not preclude an analysis using this dataset.
This is not simply a reflection of how over-represented the true ELM is in the dataset, however.
Half of the sites were invariable in the dataset; however, it is unlikely that these synonymous sites were not free to vary, which was also indicated by the poor fit with the expected distribution when only the variable sites were considered (1 rate: p<10−136 (χ2 697, df 17); 3 rates: p<10−28 (χ2 180, df 17)).
The component loadings plot for first two principal components (PCs) describing amino acid frequencies (Figure 3A) revealed the following facts:- PC1 explains 28.1% of the total variance of the dataset; however, first three PCs give a cumulative variability of 59%, exceeding to 75.7% for first 6 PCs.
The dataset, however, is much smaller in the present analysis.
This is true for all children in the dataset, however.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com