Sentence examples for using the binary classification from inspiring English sources

Using the binary classification described previously 7 patients were categorized as "Non-Responders" and 37 as "Responders" (Table 1).

The weighted κ statistic (to account for the ordered nature of the categories) was 0.79 (P<0.001) and using the binary classification (combining DY with P2 and P1 with N1) was 0.83 (P<0.001).

Thus, we used the binary classification of early stage and late stage in ovarian cancer as the phenotype or outcome (dependent variable) since stage phenotype was available for the largest number of patients in ovarian cancer from TCGA.

Using this binary classification, 4/5 uterine serous papillary cell lines and all 6 cell lines with either high HER2 (USPC1 and USPC2) or EGFR (HEC155, RL-95-2, SNG-II, and SNG-M) expression were classified as sensitive.

CDR recommendations were re-reviewed and re-classified using this more extensive system for the international comparison (replacing the binary classification used in the CDR-only analysis).

The markers show the binary classification using the Rose criteria with CNR > 3 and the lines the binomial regression curves at different background levels.

The PPV and NPV of the binary classification using the same threshold were 87.1% and 36.4%, respectively.

The sensitivity and specificity for the binary classification using the combined 2-marker panel were 77.2% and 88.9%, respectively, with a ROC AUC value of 0.92.

The benefit of using this probability rather than the binary classification is that it allows to distinguish leaf nodes in which there are mainly training individuals from one class from those in which both classes are almost equally represented.

We also evaluated the binary classification problems using the area under precision-recall curve (AUC-PR), which has been used in several earlier drug target interaction studies [ 17].

Hence, all models were also assessed in terms of the binary classification power using enrichment and area under the curve (auc) quality measures.