Sentence examples for specificity classification from inspiring English sources

In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines.

The McNemar test (Table 6) was statistically significant and there was an association between specificity classification and educational invention (p = 0.001).

Cluster analysis of patients with newly diagnosed GPA and MPA from five clinical trials showed that the ANCA specificity classification may be more strongly associated with outcomes such as death and relapse rate than the traditional GPA-MPA separation [ 23].

Diagnostic tests commonly are characterized by their true positive (sensitivity) and true negative (specificity) classification rates, which rely on a single decision threshold to classify a test result as positive.

However, even the logistic regression model incorporated the same five genes mentioned above, and in this model, the sensitivity, specificity, classification accuracy, and area under the curve (AUC) could reach 78%, 97 %, 87, and 0.906 (95% CI: 0.89 to 0.91), respectively, ander being adjusted for age, sex and smoking.

The binding specificity classification we report here is broadly similar to that generated by aligning the ETS-domain peptide sequences from multiple species using the MUST program (Laudet et al, 1999) but differs from that generated by ClustalW using just human sequences by Hollenhorst et al (2007) or by us (Supplementary Figures S4 and S5).

A ROC curve shows the relationship between the proportion of true positive (Sensitivity) and false positive (1-Specificity) classifications resulting from each possible decision threshold value in a two-class classification task [ 20].

We used various statistical measures such as Accuracy, Sensitivity, Specificity, Balanced Classification Rate (BCR) and Receiver Operating Characteristic (ROC) to evaluate the models.

This multiple genome comparison, the first such comparison in phytopathogenic bacteria as far as we know, may provide a resource for future investigation into the virulence, host specificity and classification of pseudomonads.

Thus, depending on the level of specificity of classification, the contribution of features can be either early -subserving ultra-rapid but coarse categorisation as in the studies of Thorpe and Herrmann research groups- or late -subserving entry-level categorisation that was examined in this study.

We then computed the accuracy, sensitivity, and specificity for classification performance evaluation on the testing data.