Sentence examples for predictions defined by from inspiring English sources

Specificity is the fraction of the true-positive predictions out of all the positive predictions, defined by the following formula: (6) where FP (false positive) equals the total number of the predicted clusters minus TP.

Sensitivity is the fraction of the true-positive predictions out of all the true predictions, defined by the following formula: (5) where TP (true positive) is the number of the predicted complexes matched by the known complexes with OS Pc,Kc) ≥0.2, and FN (false negative) is the number of the known complexes that are not matched by the predicted complexes.

TPR is the proportion of the true positive predictions out of all the true predictions, defined by the following formula [ 18]: (4) TPR = TP TP + FN, where true positive (TP) is the number of correctly classified and false negative (FN) is the number of incorrectly rejected entities.

Meanwhile, to cope with the circumstance such as the swerve of the mobile device, we randomly select 80% particles that participate in the prediction defined by Equation 12, and let the rest 20% particles randomly move in the circular area, with the last estimation as center and vmax as radius.

The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action.

misfit associated with those predictions is defined by: (3)where stands for the Gauss coefficients provided by CHAOS-2s in 2004.

Thresholds that correspond to the minimum number of false positive predictions as defined by minFP profiles in Transfac were used.

Compared to mimotopes recovered from Sanger sequencing of plated colonies from the same sorting protocol, motifs derived from sets of the NGS data improved epitope prediction as defined by sensitivity and precision, from 18%to82%2% and 0.27 to 0.51 for trastuzumab and 47% to 76% and 0.19 to 0.27 for bevacizumab.

(L_{2} -loss is median of (|hat{beta }-beta_{0}|_{2}) to evaL_{2} -lossstisation accuracy, and PE is the prediction error defined by median of (n^{-1}|hat{betaeta}|^{2}).

The criterion for winning the game was the prediction error defined by the expected value of the continuous rank probability score [10] for continuous outcomes.

The improvement in overall ADR prediction performance defined by AUC is significant (p-value = 1.80e-18, based on t-test).