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The lda function (having CV = True) was executed 50 times on each dataset (value imputations were applied each time to the unified datasets), in order to assess the variability of the attained accuracy performance for the melanoma class.
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In each dataset, a value Q was calculated by taking the maximum of the gene scores in the pathway.
For this analysis, the dependent variable is whether the data is from a derivation dataset (value = yes) or is from the validation dataset (value = no).
For each subject within each dataset, RT values were standardized across trials prior to GLM estimation (i.e., each RT value was demeaned and divided by the standard deviation).
This involves creating multiple copies of the data and imputing the missing values for each dataset with sensible values randomly selected from their predicted distribution.
For the English dataset, values between PESQ and P.563 are also shown to be significantly different.
All analyses were carried out on normalized and log2 transformed dataset values.
The CMap dataset values were processed as previously described [ 29].
In the excluded dataset, values falling above or below the plausibility range were excluded from analysis.
As such, four mean utilities were obtained from each dataset using the value sets from: the UK, 16 the Netherlands, 17 Germany, 18 and Spain.
For each Dataset, the average value of the Z-score across all Models (1-5 from Table 2, rounded to the nearest whole number).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com