<p>All <i>in silico</i> experiments were evaluated with 10-fold cross-validation. TP means an instance in the positive set (COSMIC) was correctly classified as causative, TN means an instance in the negative set (dbSNP) was correctly classified as non-causative.</p
<p>Confusion matrix indicating the technological application or origin prediction of 172 strains and...
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
<p><i>TP</i> is the number of correct predictions that an instance is positive (<i>true positive</i>...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negat...
<p>Confusion matrix for the classifiers of RF, SVM, and WKNN using the input dataset with all the pr...
<p>The classification results of PSOBP and BP neural network in one 10-fold cross-validation experim...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
<p>The figure shows performance measures calculated for a multiclass random forest classifier that a...
<p>Confusion matrix indicating the technological application or origin prediction of 172 strains and...
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
<p><i>TP</i> is the number of correct predictions that an instance is positive (<i>true positive</i>...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negat...
<p>Confusion matrix for the classifiers of RF, SVM, and WKNN using the input dataset with all the pr...
<p>The classification results of PSOBP and BP neural network in one 10-fold cross-validation experim...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
<p>The figure shows performance measures calculated for a multiclass random forest classifier that a...
<p>Confusion matrix indicating the technological application or origin prediction of 172 strains and...
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
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