# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 3 Experiments and Results
### Performance Comparison
#### 3.3.1 Comparison with Other Methods  
Tables 2 and 3 show the comparative performance of the different methods regarding precision and recall. The comparison results show that the DWPNMLE in our research outperforms the other two construction methods regarding precision and recall performance metrics on the two datasets where the four complex identification algorithms are applied. On the DIP dataset, the DWPNMLE achieved optimal precision and recall values of 0.7315 and 0.7156 when using the ClusterONE algorithm for complex identification, which are 10% and 11.4% higher than the ST-APIN method and 7.6% and 16.7% higher than the FS-DPIN method. In addition, when combined with the three protein complex identification algorithms, COACH, MCL, and Core, respectively, the Precision values of the DWPNMLE method were 14.3%, 29.5%, and 17.7% higher than those of the ST-APIN method and 8.5%, 14% and 10.1% higher than those of the FS-DPIN method. Meanwhile, the Recall values were improved by 28.3%, 34.8%, and 33.3% over the ST-APIN method and 3.2%, 22.6%, and 12.6% over the FS-DPIN method, respectively. On the BioGRID dataset, when the ClusterONE algorithm was used for complex recognition, DWPNMLE obtained the optimal precision value of 0.6872 and a recall value of 0.6935, which are improved by 11.6% and 23.1%, respectively, over the ST-APIN method and by 4.3% and 15.2% over the FS-DPIN method. In addition, when combined with the three complex recognition algorithms COACH, MCL, and Core, the precision values of the DWPNMLE method are improved by 17.8%, 20.3%, and 23.8% compared to the ST-APIN method and by 14.9%, 6%, and 15.1% compared to the FS-DPIN method. The recall values are improved by 31.2%, 18%, and 41.3% over the ST-APIN method and by 25.3%, 15.8%, and 16.5% over the FS-DPIN method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Recognition Methods** & **Construction Methods** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{ClusterONE} & ST-APIN & 0.6653 & 0.6422 \\  & FS-DPIN & 0.6801 & 0.6134 \\  & DWPNMLE & 0.7315 & 0.7156 \\ \hline \multirow{3}{*}{COACH} & ST-APIN & 0.5977 & 0.4887 \\  & FS-DPIN & 0.6300 & 0.6072 \\  & DWPNMLE & 0.6834 & 0.6269 \\ \hline \multirow{3}{*}{MCL} & ST-APIN & 0.4905 & 0.4165 \\  & FS-DPIN & 0.5568 & 0.4580 \\ \cline{1-1}  & DWPNMLE & 0.6350 & 0.5613 \\ \hline \multirow{3}{*}{Core} & ST-APIN & 0.5520 & 0.4676 \\ \cline{1-1}  & FS-DPIN & 0.5903 & 0.5536 \\ \cline{1-1}  & DWPNMLE & 0.6498 & 0.6233 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different methods on the DIP dataset. The results of the comparison of F1-measure values are shown in Figures 3 and 4, which clearly show that the DWPNMLE method has the highest F1-measure value. On the DIP dataset, the DWPNMLE achieves the optimal F1-measure value of 0.7235 when using the ClusterONE algorithm for complex identification, which is 10.7% higher than ST-APIN (0.6535) and 12.2% higher than FS-DPIN (0.6450). In addition, the DWPNMLE also obtains the highest F1-measure values when combines with the three complex recognition algorithms, COACH, MCL, and Core, with 0.6539, 0.5959, and 0.6363, respectively, followed by the FS-DPIN method, with F1-measure values of 0.6184, 0.5026, and 0.5714. For the third-ranked ST-APIN method, the corresponding F1-measure values are 0.5377, 0.4505, and 0.5063. On the BioGRID dataset, the DWPNMLE achieves the optimal F1-measure value of 0.6903 when using the ClusterONE algorithm for complex recognition, which is 16.8% higher than that of the ST-APIN (0.5909) and 9.7% higher than that of the FS-DPIN (0.6294). The DWPNMLE likewise obtained the optimal F1 value. The following best F1 measure values are obtained by the FS-DPIN, with the corresponding F1 values of 0.5230, 0.4605, and 0.5030. The third-best F1-measure values are obtained by the ST-APIN, at 0.5044, 0.4293, and 0.4384, respectively.