<p>Effect of network size on network reconstruction – AUC values of the ROC (A) and the PR (B) curves of the network inference using the LP model, the DEPNs and random guessing, for different network sizes. Results were obtained using stratified 10-fold crossvalidation. Calculation with DEPN did not finish within 1000 hours of computation time for networks of size , and computations were thus interrupted.</p
<p>Impact of sample size, experimental noise and algorithm selection on network recovery.</p
<p>Effect of perturbation data on network reconstruction performance (a and b represent AUPR and AUR...
<p><i>P</i>-values for the area under the ROC curve for each of the five networks in the size-100 su...
<p>The figure shows the area under the receiver operator characteristic (AUC ROC) and area under the...
<p>The Figure shows receiver operator characteristic (ROC) and precision to recall curves (PR) for n...
<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7),...
<p>Overview of all results on the simulated and biological datasets, using the approach presented in...
<p>The figure shows the computation time required to infer networks of different sizes, for the LP (...
<p>The influence of the <i>equal width</i> discretization method on the global network inference per...
well across sizes.We provided subchallenges with networks of size 10, 50, and 100 in order to compar...
<p>The influence of the <i>global equal width</i> discretization method on the global network infere...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
<p>Impact of sample size, experimental noise and algorithm selection on network recovery.</p
<p>Effect of perturbation data on network reconstruction performance (a and b represent AUPR and AUR...
<p><i>P</i>-values for the area under the ROC curve for each of the five networks in the size-100 su...
<p>The figure shows the area under the receiver operator characteristic (AUC ROC) and area under the...
<p>The Figure shows receiver operator characteristic (ROC) and precision to recall curves (PR) for n...
<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7),...
<p>Overview of all results on the simulated and biological datasets, using the approach presented in...
<p>The figure shows the computation time required to infer networks of different sizes, for the LP (...
<p>The influence of the <i>equal width</i> discretization method on the global network inference per...
well across sizes.We provided subchallenges with networks of size 10, 50, and 100 in order to compar...
<p>The influence of the <i>global equal width</i> discretization method on the global network infere...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
The network autocorrelation model has become an increasingly popular tool for conducting social netw...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
<p>Impact of sample size, experimental noise and algorithm selection on network recovery.</p
<p>Effect of perturbation data on network reconstruction performance (a and b represent AUPR and AUR...
<p><i>P</i>-values for the area under the ROC curve for each of the five networks in the size-100 su...
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