<p>(a) ACC, (b) BACC, (c) MCC, and (d) AUC of five SVM models trained on 5 different data sets (train_10, train_30, train_50, train_70, and train_90) tested by 9 different testing data sets, ranging from 10% deleterious (x-axis = 0.1) to 90% deleterious (x-axis = 0.9).</p
<p>The Performance of SVM Models on validation dataset with experimentally derived binding affinity ...
<p>“*” denotes the GMM+MS is significantly better than t-test approach at a statistical level of 0.0...
<p><b>Sn:</b> Sensitivity; <b>Sp:</b> Specificity; <b>Acc:</b> Accuracy; <b>MCC:</b> Matthew’s Corre...
<p>(a) TPR, (b) NPR, (c) PPV, and (d) NPV of five SVM models trained on 5 different data sets (train...
<p>(a) Values for TPR, TNR, PPV, and NPV. (b) Values for MCC, BACC, AUC, and ACC.</p
<p>Box plots illustrating differences in performance (AUC and MCC) of 10 optimized SVM component mod...
<p>The Performance of SVM Models on PSSM based training dataset D3 & D4 with different learning para...
Accuracy comparisons of ten experiments with SVM, KNN, BPNN, CNN, ResNet, FA+ResNet models.</p
<p>Using binary patterns and AA (amino acid) composition [γ <b>(g)</b> (in RBF kernel), c: parameter...
<p>Performance of four SVM modules (AAC, DPC, SAAC, Hybrid) by the receiver operating characteristic...
<p>Total 1783 cysteine sequences were applied in positive and negative data. Sn, sensitivity; Sp, sp...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
Shown are the dummy PREdictor, the PyPREdictor trained with PREs (positives) and dummy PREs (negativ...
<p>ROC curves of the single SVM models trained using various features based on five-fold cross-valid...
<p><b>Copyright information:</b></p><p>Taken from "Analysis of nanopore detector measurements using ...
<p>The Performance of SVM Models on validation dataset with experimentally derived binding affinity ...
<p>“*” denotes the GMM+MS is significantly better than t-test approach at a statistical level of 0.0...
<p><b>Sn:</b> Sensitivity; <b>Sp:</b> Specificity; <b>Acc:</b> Accuracy; <b>MCC:</b> Matthew’s Corre...
<p>(a) TPR, (b) NPR, (c) PPV, and (d) NPV of five SVM models trained on 5 different data sets (train...
<p>(a) Values for TPR, TNR, PPV, and NPV. (b) Values for MCC, BACC, AUC, and ACC.</p
<p>Box plots illustrating differences in performance (AUC and MCC) of 10 optimized SVM component mod...
<p>The Performance of SVM Models on PSSM based training dataset D3 & D4 with different learning para...
Accuracy comparisons of ten experiments with SVM, KNN, BPNN, CNN, ResNet, FA+ResNet models.</p
<p>Using binary patterns and AA (amino acid) composition [γ <b>(g)</b> (in RBF kernel), c: parameter...
<p>Performance of four SVM modules (AAC, DPC, SAAC, Hybrid) by the receiver operating characteristic...
<p>Total 1783 cysteine sequences were applied in positive and negative data. Sn, sensitivity; Sp, sp...
<p>Measured by the area under the ROC curve (AUC), classification performance is shown for models co...
Shown are the dummy PREdictor, the PyPREdictor trained with PREs (positives) and dummy PREs (negativ...
<p>ROC curves of the single SVM models trained using various features based on five-fold cross-valid...
<p><b>Copyright information:</b></p><p>Taken from "Analysis of nanopore detector measurements using ...
<p>The Performance of SVM Models on validation dataset with experimentally derived binding affinity ...
<p>“*” denotes the GMM+MS is significantly better than t-test approach at a statistical level of 0.0...
<p><b>Sn:</b> Sensitivity; <b>Sp:</b> Specificity; <b>Acc:</b> Accuracy; <b>MCC:</b> Matthew’s Corre...