<p>Results for the functional annotation for each graph representation using timeBGLL, showing the ROC curve (A) with H indicating the corresponding entropy value and the corresponding AUC values (B).</p
<p>ROC curve and AUC values for CBFS and Lect feature selection algorithms on Prostate dataset.</p
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>The ROC curve of the computational method based on the tetrahedron, HOG and Gaussian RBF neural n...
<p>Results for the functional annotation for each graph representation using BGLL, showing the ROC c...
<p>Results for the functional annotation for each graph representation using FC, showing the ROC cur...
<p>Results for the functional annotation for each graph representation using Infomap, showing the RO...
<p>Results for the functional annotation for each graph representation using edgeCluster, showing th...
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>Values for the AUC for the functional annotation with each clustering algorithm and graph represe...
<p>The ROC graph is plotted to show the performance of the binary classifiers.</p
<p>TE-b/LA1-b/LA2-b are the results by binning entropy estimator and TE-n/LA1-n/LA2-n by NN entropy ...
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>The ROC curves of the computational method based on the Z-curve, HOG and MLP neural network.</p
<p>The ROC curve of the computational method based on the Z-curve, HOG and Gaussian RBF neural netwo...
ROC curves for the mortality model, using test data (AUC values are indicated on the graph).</p
<p>ROC curve and AUC values for CBFS and Lect feature selection algorithms on Prostate dataset.</p
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>The ROC curve of the computational method based on the tetrahedron, HOG and Gaussian RBF neural n...
<p>Results for the functional annotation for each graph representation using BGLL, showing the ROC c...
<p>Results for the functional annotation for each graph representation using FC, showing the ROC cur...
<p>Results for the functional annotation for each graph representation using Infomap, showing the RO...
<p>Results for the functional annotation for each graph representation using edgeCluster, showing th...
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>Values for the AUC for the functional annotation with each clustering algorithm and graph represe...
<p>The ROC graph is plotted to show the performance of the binary classifiers.</p
<p>TE-b/LA1-b/LA2-b are the results by binning entropy estimator and TE-n/LA1-n/LA2-n by NN entropy ...
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>The ROC curves of the computational method based on the Z-curve, HOG and MLP neural network.</p
<p>The ROC curve of the computational method based on the Z-curve, HOG and Gaussian RBF neural netwo...
ROC curves for the mortality model, using test data (AUC values are indicated on the graph).</p
<p>ROC curve and AUC values for CBFS and Lect feature selection algorithms on Prostate dataset.</p
<p>Values for the sensitivity (sens.) and the false positive rate (fpr), for the functional annotati...
<p>The ROC curve of the computational method based on the tetrahedron, HOG and Gaussian RBF neural n...