Conformation generation method.

<p>Conformation generation method.</p

Confusion matrix.

<p>TP: true positives, FP: false positives, FN: false negatives, TN: true negatives.</p

Evolution of AUC, true negative rate (TNR) and false negative rate (FNR) in CF-GA using increasing number of classifiers.

<p>Classifiers were added to the collaborative filter, using averaged voting, in increasing precision order. For example, abscissa “JRIP-N5” corresponds to the CF-GA method using J48-M10, Logistic and JRIP-N5 classifiers. Green and red curves correspond to AUC of GA method (which is constant since it doesn't use the classifiers, shown for comparison) and CF-GA method respectively. TNR: true negative rate; FNR: false negative rate.</p

True positive rate for uniform and weighted collaborative filtering.

<p>The true positive rate (green) and the total number of positives are plotted for uniform (left) and weighted (right) collaborative filtering, as a function of the category. The vertical and horizontal dotted lines give the category, and the corresponding number of conformations predicted as positives, above which the true positive rate decreases under 1.</p

Enrichment in acceptable or better solutions.

<p>The enrichment in acceptable or better conformations (E) is computed as the proportion of such conformations in the 20% best ranked conformations (respectively worst ranked conformations) divided by the proportion of such conformations is the complete set. Same computation for medium quality or better conformations (E). These enrichments are computed using either or CAPRI criteria (), and for the three methods (GA: genetic algorithm, CF: collaborative filtering, CF-GA: hybrid method). Values in italic are not statistically significant.</p

Comparison between and evaluations.

<p>For all the conformations in the CAPRI scoring ensembles, the classifications as high-quality, medium-quality, acceptable or incorrect conformation using only , or complete CAPRI are compared. For example, there are 298 conformations classified as medium-quality using CAPRI criteria and high-quality by criterion.</p

Evaluation of the CF-GA method.

<p>Best quality conformation found in the top 10 ranked solutions from target 22 to target 40 for genetic algorithm (GA), collaborative filtering (CF) and combination of the previous two (CF-GA) methods, with RMSD filtering. Same results are given for the CF-GA method without RMSD filtering, and for the <i>CF then GA</i> method. N: Numbers of acceptable or better solutions in the top 10; R: rank of the first acceptable or better solution for each target. Numbers of high quality (), medium quality (), acceptable () and incorrect conformations in each ensemble and for each method when using RMSD filtering are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018541#pone-0018541-t004" target="_blank">Table 4</a>.</p

Best conformation present in the top 10 for different scoring groups.

<p>: no acceptable or better solution found, -: group has not participated,</p>a<p>: evaluation.</p

Genetic Algorithm performance as a function of the number of runs.

<p>For each number of runs , the measure of the AUC has been repeated 50 times using a 10-fold cross-validation protocol. Average, minimum and maximum values are plotted.</p

Scoring results on the unbound test set.

<p>Enrichment Score, 10 best energy candidates, 100 best energy candidates, number of near-native structures and Area Under the ROC Curve are reported for each native structure both using the non-optimized RosettaDock scoring function (Default) and our optimized scoring function (ROGER).</p><p>Scoring results on the unbound test set.</p