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Moreover, between the different data sources, the relationships between data score and co-complexness are different.
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33, 34 In this study, LM training algorithm is applied to discover the relationship between antifungal activity score data for a dataset of coumarins antifungal with the thermodynamic and physico-chemical descriptors.
In contrast, in the high-STAT1 group there was no correlation between STAT1 and IFN score (data not shown).
There were no differences in efficiency scores between data envelopment analysis (DEA) and stochastic frontier analysis (SFA) techniques.
We used this rather stringent threshold to compensate for the inherent correlation in the scores between data sets (for e.g., we have multiple REV-ERB and BMAL1 ChIP-seq data) that is unaccounted for in the shuffling procedure.
Spearman correlations between motor performance scores and growth data, intelligence scores, behavioural scores and socio-economic status were calculated.
When calculating the Silhouette score, the distance between data points can be measured either in one dimension, for example on the x-axis, or in two dimension using vectors, as illustrated in Figure 1.
For comparisons between questionnaire data, raw scores of headache frequency obtained from the diaries were categorized into three levels: ≤10 %, 11 49 %, and ≥50%% of the days.
Inter-rater reliability between data extractors was tested on CIRS scores and number of domains with morbidities for 30 randomly selected patients from each of the two cohorts.
General linear model univariate analysis was employed to compare data between attitudinal score and each independent norminal demographic variable.
We observed that summed criteria scores >1 resulted in significant disparity between the empirical and modeled data, while scores between 0.5 and 1 signified only moderate differences.
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