Sentence examples for accuracy of respectively from inspiring English sources

FES may be more accurate in distinguishing LMS from fibroids than FDG, with an accuracy of respectively 93 and 81%[67]7].

These PSOs nominally consist of both a reduced-dynamic and a kinematic orbit solution, which have an accuracy of, respectively, better than 2 cm and about 4 5 cm, as shown by independent satellite laser ranging (SLR) validation (van den IJssel et al. 2015).

Best prediction was obtained with a cutoff of SUVmax=3, which allowed identification of poor responders (non-pCR) with a sensitivity, specificity, PPV, NPV and overall accuracy of, respectively: 85.7% (12 out of 14), 93.8% (15 out of 16), 92.3% (12 out of 13), 88.2% (15 out of 17) and 90% (27 out of 30).

The same method applied to 1000 label-permuted datasets never exceeded 65% accuracy with a median and minimum accuracy of 49%and39.3%3% respectively.

In our study, we built the SVM-based porcine pre-miRNAs classifier with a sensitivity of 100%, a specificity of 91.2% and a total prediction accuracy of 95.6%, respectively.

Results from the case study show that SVM classification using hyper-temporal RS imagery was more effective in modeling both soil texture and coarse fragment classes relative to mono-, bi-, or multi-temporal RS, with classification accuracies of 67%and62%2%, respectively.

Recently the performance of prediction methods of variation pathogenicity on missense variants was assessed and two methods, SNPs&GO [ 4] and MutPred [ 5] scored with accuracies of 82%and81%1%, respectively [ 6].

It appears that both SIFT [Ng and Henikoff, 2001] (another sequence-based method) and our unweighted method perform less favorably with accuracies of 65%and69%9%, respectively, indicating that FATHMM is somewhat the better option of the two.

In contrast, the cross validation performed by Ross et al [ 14] using ANN resulted in additional misclassifications of samples from the hyperdiploid and the TEL-AML1 subgroup, with prediction accuracies of 95%and96%6%, respectively.

We draw Fig. 5 (respectively Fig. 6) to compare a pure SVM with our approach in term of accuracy rate (respectively in term of the number of selected variables) point of view.

Evaluations carried out with the CASIA Face Anti-Spoofing Database and NUAA Photograph Imposter Database (http://parnec.nuaa.edu.cn/xtan/data/NUAAImposterDB.html) showed an equal error rate of 17% and an accuracy of 86%, respectively.