++<p>, <sup>−+</sup>, <sup>−−</sup>, <sup>+−</sup> denote true positive, false positive, false negative, and true negative, respectively.</p
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
<p>All <i>in silico</i> experiments were evaluated with 10-fold cross-validation. TP means an instan...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
<p>(a-b) These classification performances are achieved by [Ray et al., 07], (c-d) These classificat...
<p>TP: true positives, FP: false positives, FN: false negatives, TN: ...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>Confusion matrix and overall performance of the classifier used to determine the sharpness of the...
<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...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
<p>TP = true positive; FP = false positive; FN = false negative; TN = true negative.</p
<p>Confusion matrix—observed and predicted values obtained using the Cavnar and Trenkle procedure fo...
<p>(A) The left oval shows two actual labels: positives (P; blue; top half) and negatives (N; red; b...
A) Logistic Regression with MMP8 and B) Gradient Tree Boosting with CXCL1, CXCL2, TNF, and IL-10. Ro...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
<p>All <i>in silico</i> experiments were evaluated with 10-fold cross-validation. TP means an instan...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
<p>(a-b) These classification performances are achieved by [Ray et al., 07], (c-d) These classificat...
<p>TP: true positives, FP: false positives, FN: false negatives, TN: ...
<p>Here, TP (true positive) and TN (true negative) denote the number of correctly predicted overlap ...
<p>Confusion matrix and overall performance of the classifier used to determine the sharpness of the...
<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...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
<p>TP = true positive; FP = false positive; FN = false negative; TN = true negative.</p
<p>Confusion matrix—observed and predicted values obtained using the Cavnar and Trenkle procedure fo...
<p>(A) The left oval shows two actual labels: positives (P; blue; top half) and negatives (N; red; b...
A) Logistic Regression with MMP8 and B) Gradient Tree Boosting with CXCL1, CXCL2, TNF, and IL-10. Ro...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p>Classification errors (1-AUC) for classifiers trained for one class against another class. All pa...
<p>All <i>in silico</i> experiments were evaluated with 10-fold cross-validation. TP means an instan...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
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