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The predictive capacities of both models were successfully validated by calculating a test set of 13 molecules that were not included in the training set.
Statistical inconsistency within both subgroups was assessed by calculating a test of heterogeneity (I square (I)).
We tested the "independent breakage" hypothesis by calculating a test statistic β, the count of joint breaks in homologous blocks, and computing its significance based on the values of β in a 1000 random matching of blocks across the two variable species in a length-restricted fashion.
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We assessed heterogeneity of trial results by calculating a chi-square test of heterogeneity and the I2 measure of inconsistency.
We assessed heterogeneity of the studies' results by inspecting graphical presentations and by calculating an x test of heterogeneity and the I 2 statistic of inconsistency [ 19, 20].
We correct for that by calculating a multiple hypothesis testing corrected p-value for each pattern and threshold.
Significant difference in the odds ratios for gout per sugar-sweetened beverage category between body mass index groups was assessed by calculating a Cochran's Q test statistic and the corresponding P value (PQ).
The statistical significance of modulation of gamma amplitude by theta phase was tested by calculating a surrogate distribution obtained by creating 1000 sets of randomly-shuffled theta-phase time series and computing the mean gamma amplitudes within each phase bin in the same way.
This will be tested by calculating a Cronbach's alpha for each construct.
The equality of ICCs was tested by calculating a two-sample t statistic using the estimated ICCs and their estimated standard error.
Initially, loess and Aquantile normalization was performed, followed by calculating a moderated Student's t-test using the Linear models for micro array (Limma) package [ 47].
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