# LPBERT: A Protein-Protein Interaction Prediction Method Based on a Pre-Trained Language Model
## 3 Results
### Comparative Experiment  
To evaluate the prediction performance of LPBERT in existing mainstream protein-sequence-based computational methods, this study compared LPBERT with nine protein-sequence-based computational methods that emerged in recent years, and the experimental results are discussed in detail in this section. We reproduced the three methods of PIPR, RAPPPID, and DeepTrio. In order to compare the performance of LPBERT with them, we classified part of the original dataset as an independent test set and used the rest of the data for training and validation. To ensure experimental objectivity and reliability, all methods were trained and tested on identical datasets using parameters specified in their respective original papers. Tables 4 and 5 present the comparative performance results on the BioGRID test set. Tables 4 and 5 show that all methods performed effectively regarding PPI prediction using the BioGRID dataset, where they achieved high scores across multiple metrics. LPBERT, which leverages a pre-trained protein language model for sequence embedding extraction, outperformed all other methods across all metrics, with DeepTrio ranking second in overall performance. On the _H. sapiens_ dataset, LPBERT achieved an accuracy 3.24% higher than DeepTrio, while on the _S. cerevisiae_ dataset, LPBERT's accuracy exceeded DeepTrio's by 5.17%. These results demonstrate LPBERT's effectiveness in PPI prediction. At the same time, we conducted a comparative analysis to evaluate the performance differences between the LPBERT and PPI prediction methods based on Word2 Vec and Seq2 Vec for sequence encoding, as well as multi-feature fusion PPI prediction methods that integrate multi-dimensional information, such as protein sequence physicochemical properties and amino acid sequences. The results of the comparative experiment are shown in Table 6.  
By observing the experimental results in Table 6, we can find that the accuracy of LPBERT was 0.05% higher than that of DeepFE-PPI [40] and 3.57% higher than that of HNSPPI [41]. This result shows that the pre-trained protein language model had a slight advantage over single-encoding methods, such as Word2 Vec and Seq2 Vec. However, LPBERT showed performance limitations when compared with PPI prediction methods that integrate multiple sequence processing technologies. Both SDNN-PPI [42] and DeepCF-PPI [43], which combine multiple sequence information dimensions, demonstrated superior performance. SDNN-PPI utilizes traditional sequence feature representation, while DeepCF-PPI enhances this method by incorporating Word2 Vec technology. In comparative evaluations, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **MCC** \\ \hline PIPR & 94.50 \(\pm\) 0.22 & 95.81 \(\pm\) 0.28 & 93.08 \(\pm\) 0.20 & 95.93 \(\pm\) 0.28 & 94.42 \(\pm\) 0.22 & 89.04 \(\pm\) 0.43 \\ RAPPPID & 94.16 \(\pm\) 2.04 & 95.10 \(\pm\) 2.30 & 93.15 \(\pm\) 2.23 & 95.17 \(\pm\) 2.34 & 94.10 \(\pm\) 2.06 & 88.36 \(\pm\) 4.08 \\ DeepTrio & 95.69 \(\pm\) 0.33 & 98.57 \(\pm\) 0.36 & 92.73 \(\pm\) 0.37 & 98.65 \(\pm\) 0.34 & 95.56 \(\pm\) 0.34 & 91.54 \(\pm\) 0.65 \\ LPBERT & **98.93 \(\pm\) 0.13** & **99.23 \(\pm\) 0.19** & **98.62 \(\pm\) 0.13** & **99.23 \(\pm\) 0.19** & **98.93 \(\pm\) 0.13** & **97.85 \(\pm\) 0.26** \\ \hline \hline \end{tabular}  
This table reports the mean and standard deviation of the three test results. The best values are in bold.  
\end{table} 
Table 4: Comparison of each method on the BioGRID _H. sapiens_ dataset (%).