Sentence examples for the predictor achieved from inspiring English sources

The maximum asymptote parameter (D) was constrained at 1 in order to reflect the high accuracy the predictor achieved with recent FFPE samples.

In our previous work, Xia et al. [ 7] presented a novel disease-resistant gene predictor by using chaos games representation (CGR), and the predictor achieved a high accuracy of 98.13% by using a small database with 107 samples.

While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure.

None of the predictors achieved an MCC above 0.3.

Post-implementation, however, the correlation of this multivariate model is negligible (r 2 ≈ 0.029) and none of the predictors achieve statistical significance.

As shown in Table  6, Figures  11 and 12, the CLCLpred predictor achieved the best ACC and AUC.

This feature selection process is illustrated in Figure 1; from Figure 1 it can be seen that the DX-RF predictor achieved the highest 87.97% accuracy when adopting the 11 top-ranked features and the mRMR-RF predictor also got a similar highest 88.18% accuracy with the 76 top-ranked features.

While the consensus human predictor achieved very slightly better headline figures on various statistical measures, the difference between it and the consensus machine learning predictor was both small and statistically insignificant.

Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient).

The experimental result showed that our predictor achieved an overall MCC of 0.7063.

A consensus predictor achieved 0.61 MCC and 80.0% ACC by averaging the scores from the three methods.