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Exact(4)
In our study, K method yielded good quality DNA than other five methods.
In the current study, BP1 method yielded good quality DNA compared to BP2 method.
When predicting PPIs of Human dataset, the SVM-based method yielded good results with the average accuracy, precision, sensitivity, MCC, and AUC of 85.33%, 86.92%, 81.59%, 74.81%, and 93.15%, respectively.
When predicting PPIs of Human dataset, the proposed method yielded good results of average accuracy, precision, sensitivity, and MCC of 96.30%, 99.59%, 92.63%, and 92.82% and the standard deviations are 0.10%, 0.29%, 0.44%, and 0.19%, respectively.
Similar(55)
The method yields good results for some validation parameters.
All results show that our method yields good sensitivity, accuracy and F1 score comparing with the existing methods.
From Table 8, it can be observed that our method yields good results similar to or even better than some other existing methods based on ensemble classifiers.
There is good inter-observer and intra-observer agreement and, compared to a molecular genetic gold-standard, the method yields good sensitivity, specificity, positive predictive value and negative predictive value as a predictor of methylation status.
A 10 × 10-fold cross-validation experiment with 2346 genes having both, a KEGG annotation and a unique protein-domain signature, shows that our method yields good classification performance.
Some application methods yielded good shear strength but low fatigue life.
Only BayesNet (BN), NaiveBayesUpdateable (NBU), RBFNetwork (RBFN), DecisionTable (DT), and J48 methods yield good results.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com