## 3 Results
### Result for SOS Network in E. coli  
The SOS network [29] is a signal pathway in the DNA repair system. It is inferred from real gene expression data and is frequently used to test the effectiveness of network inference methods. Here, we test the DCNTC on the network of _E. coli_. Table 4 shows the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **TP** & **FP** & **TN** & **FN** & **TPR** & **FPR** & **PPV** & **ACC** & **MCC** \\ \hline \hline CLR & 19 & 165 & 983 & 58 & 0.247 & 0.144 & 0.103 & 0.818 & 0.070 \\ ARACNE & 13 & 125 & 1023 & 64 & 0.170 & 0.109 & 0.094 & 0.846 & 0.046 \\ MRNET & 21 & 215 & 933 & 56 & 0.273 & 0.187 & 0.089 & 0.779 & 0.053 \\ REL-DC & 34 & 49 & 1099 & 43 & 0.442 & 0.043 & 0.410 & 0.925 & 0.385 \\ MRNET-DC & 70 & 465 & 683 & 7 & 0.909 & 0.405 & 0.131 & 0.615 & 0.247 \\ LDCNET & 23 & 26 & 1122 & 54 & 0.299 & 0.023 & 0.469 & 0.935 & 0.342 \\ DCNTC & **29** & **20** & **1128** & **48** & **0.377** & **0.017** & **0.592** & **0.945** & **0.445** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of different methods for inferring a 50-gene network in DREAM3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **TP** & **FP** & **TN** & **FN** & **TPR** & **FPR** & **PPV** & **ACC** & **MCC** \\ \hline \hline CLR & 39 & 713 & 4071 & 127 & 0.235 & 0.149 & 0.052 & 0.830 & 0.044 \\ ARACNE & 20 & 417 & 4367 & 146 & 0.121 & 0.087 & 0.046 & 0.886 & 0.403 \\ MRNET & 49 & 984 & 3800 & 117 & 0.295 & 0.206 & 0.047 & 0.778 & 0.040 \\ REL-DC & 121 & 386 & 4398 & 45 & 0.729 & 0.081 & 0.239 & 0.913 & 0.385 \\ MRNET-DC & 145 & 2011 & 2773 & 21 & 0.874 & 0.421 & 0.067 & 0.590 & 0.165 \\ LDCNET & 45 & 54 & 4730 & 121 & 0.271 & 0.011 & 0.455 & 0.965 & 0.334 \\ DCNTC & **55** & **42** & **4742** & **111** & **0.331** & **0.009** & **0.567** & **0.969** & **0.419** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the performance of different methods for inferring a 100-gene network in DREAM3.  
results of the seven methods applied to the SOS network in the _E. coli_ dataset. The results show that the method outperforms all other methods except MRNET and LDCNET in terms of MCC. Although our method did not identify the most correct edges, it produced the least redundant edges of most methods. Furthermore, the validity of the model on the SOS network was not satisfactory compared to previous experiments on other datasets. The main reason for this finding is related to the characteristics of the SOS network and is due to two main factors: firstly, noise in the real data can be taken into account, making the calculation of node centrality inaccurate and leading to biases in the relative positions between gene regulation, resulting in fewer true-positive edges being predicted; secondly, the SOS network consists of nine nodes and 22 edges. In the final construction of the network, the algorithm selects only the connected edges with the most important relationships to the target gene regulation, which indirectly limits the count of boundaries in the prediction network to eight. Although the experimental results are not ideal, Table 4 shows that our method is no more effective than any other test method other than MRNET and LDCNET.