Individual baseline values and median levels of the six biomarkers are shown in Figure 2. The computed specificities, sensitivities, positive and negative predictive values, and AUCs of the single markers and the composite markers with regard to diagnosis of bacterial infection are shown in Table 3.
The first step to computing Specificity is to construct a set of comparable HYPs (see below).
Given the benefits offered by this method of computing Specificity, it was carried forward for this study.
Annotated pseudogenes or proteins of non-reference species were not considered when computing specificity, sensitivity, and Jaccard indices.
These relevance/non-relevance ratings of users for the results of various ranking algorithms were used to compute specificity and sensitivity scores.
We computed the specificity of a UniRef cluster as the maximum specificity P (Equation (1)) for all GO molecular function terms from cluster's members.
The cut-off value of 2D-MRE for classifying advanced fibrosis was selected to optimize sensitivity with specificity at 90% or greater; for that cut-off value, the following performance parameters were computed: sensitivity; specificity; positive predictive value (PPV); negative predictive value (NPV); and total accuracy.
To build the miRNA risk factor model, we used miRNAs that were validated in all cohorts with the same effect directions, and we computed sensitivity, specificity, accuracy, and area-under-the-receiver operating characteristic-curve (AUC) to assess performance of risk factors.
By means of true-positive, true-negative, false-positive, and false-negative rates, we computed sensitivity, specificity, positive and negative likelihood ratios, and ORs.
The following reporting characteristics with 95% exact binomial confidence interval (95%CI) were computed: sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
