Fig7: Block Transaction Count by Degree

For each block, the count of the number of transaction by their in / our degree

The comparison of the real weights and the weights estimated from different methods.

<p>The comparison of the real weights and the weights estimated from different methods.</p

The ontology built using the top 10 significant GO terms discovered from up-regulated genes for MD compared to Mglio.

<p>The ontology built using the top 10 significant GO terms discovered from up-regulated genes for MD compared to Mglio.</p

The results of our method, the regularised logistic regression (RLR) and sparse Bayesian learning (SBL) under different noise levels.

<p>The accuracy of classification, false positive and false negative rates are compared.</p

The comparison of the real weights and the weights estimated from different methods.

<p>The comparison of the real weights and the weights estimated from different methods.</p

Bar charts of RMSE for inferred transition matrices.

<p>These charts show the results of both Step 2 and Step 3 for 6 different benchmark networks with different numbers of perturbations varying from 2 to 7. In Step 2, the RMSE values range from to with the mean value of ; in Step 3, the RMSE values range from to with the mean value of . The RMSE ratios (Step 3/Step 2) vary from 0.14% to 51% with the mean value of 17%.</p

Relationship between noise levels and RMSE.

<p>This chart shows the RMSE values for inferred transition matrices of n-39 network under 6 perturbations at different noise levels. The standard deviations of noises vary from 10 to 1. In Step 2, the RMSE values range from to ; in Step 3, the RMSE values range from to .</p

Characteristics of the benchmark network set.

<p>Characteristics of the benchmark network set.</p

Relationship between the average variance and RMSE.

<p>Each point represents an experiment for a benchmark network under a specific number of perturbations. For example, 2-n-53 means the experiment for n-53 network under 2 perturbations. The x-coordinate indicates the natural logarithm of the average variance for all elements in the refined transition matrix, while the y-coordinate indicates the RMSE values of the refined transition matrix. The RMSE values range from to and the average variance varies from to .</p

ROC curves of network structure inference.

<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7), is evaluated by ROC curves. Each subplot contains the inference results for 6 benchmark networks. The average AUROC is 0.97. More specifically, the maximum AUROC value 1.0 is achieved by the n-4 network (3–7 perturbations) and the n-11 network (6–7 perturbations), while the minimum AUROC value 0.88 is obtained by the n-58 network (2 perturbations).</p