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The clustering of patients was done by the hierarchical UPGMA method [13] using a Euclidean measure of similarity.
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By computing differences in co-regulation between genes across the two datasets, using a Mutual Information measure, IDEA was able to identify dysregulated pathways in disease.
This can be done by using an RMSD measure calculated only for the superposed interface structures, while not taking into account the overall structures of the interacting subunits [3], [13], [14], [15].
To assess the validity of our categorizations, we evaluated all the detected interactions using an IG measure, a method for computationally assessing the reliability of PPI.
A large advantage of the model is the gain in power by using an AP measure that is obtained over time and raters.
For PTSD symptoms, we used a 4-item measure developed for primary care by the National Center for PTSD (Primary Care PTSD Screen, PC-PTSD) [ 17].
Here we use a Beta measure based on variation among all possible pairs of sites, rather than site to site turnover along a directional gradient [ 50].
To partition the variation in prescribing at practice and patient levels we used an R measure derived from a two level logistic regression with random intercepts.
Changes in excitability over time between the different groups and sham were compared by using a General Linear Models repeated-measures ANOVA.
The Review graded the debates using a Defense Department test that measures the level of education required to understand various reading material.
The quantity and quality of RNA were measured using a Thermo Scientific NanoDropTM 1000 spectrophotometer (Wilmington, DE, USA).
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