## 3 Results and Discussion
### Ablation Experiment  
CSpredR consists of three parts, with the first two modules using different encoding methods and neural network models for feature extraction. To verify the effectiveness of certain structures proposed in the CSpredR model, we conducted ablation experiments on the first two modules separately. In the encoding part, we compared the performance of word2 vec and fasttext individually and their combined encoding results while keeping the neural network model module unchanged. As shown in Table 2, we found that fasttext's performance was not as good as word2 vec when used alone--this could be attributed to the limitations of using fastText encoding alone compared to word2 Vec. While fastText excels at handling out-of-vocabulary words by breaking them into subword units, it struggles to effectively capture word order information, which is critical for sequence-based tasks. In contrast, word2 Vec directly encodes semantic relationships between words in a way that better preserves contextual information, making it more suitable for tasks requiring fine-grained sequence representation. but the performance improved when the two were combined. This may be because the two methods complement each other in learning word vectors and sequence representation, resulting in richer text representations. Additionally, we performed Wilcoxon tests to evaluate the predictive results of different encoding methods. As shown in Table 3, CSpredR significantly outperforms other methods across all evaluation metrics (\(p<0.05\)). Notably, CSpredR shows significant advantages in Accuracy and One-error, demonstrating higher accuracy and better label selection capability, with other metrics such as Hamming-loss, Coverage, Average precision, and Ranking loss also exhibiting relative superiority. In the neural network model part, we compared the performance of five combinations of network model structures while keeping the two encoding methods unchanged: CNN, CNN + attention, Bi-LSTM, Bi-LSTM + attention, and CSpredR. The results are shown in Table 4. We found that using CNN alone yielded unsatisfactory results, possibly because CNN only focuses on local information in the text. After adding the multi-head self-attention mechanism, the model's performance improved, likely due to the model's enhanced ability to process key information. However, Bi-LSTM outperformed CNN because it can capture long-term dependencies in the text, providing a more comprehensive text representation. When attention mechanisms were integrated into Bi-LSTM, the model's \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Hamming Loss** & **One-Error** & **ACC** & **Coverage** & **Average Precision** & **Ranking Loss** \\ \hline Word2 vec & 0.219 & 0.659 & 0.625 & 6.211 & 0.650 & 0.445 \\ Fasttext & 0.274 & 0.716 & 0.588 & 6.279 & 0.648 & 0.482 \\ CSpredR & 0.182 & 0.605 & 0.657 & 6.035 & 0.675 & 0.380 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experimental results based on different encoding methods. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Word2 vec** & **Fasttext** \\ \hline \(p\)-value of Hamming loss & 0.03125 & 0.00484 \\ \(p\)-value of One-error & 0.03659 & 0.03496 \\ \(p\)-value of ACC & 0.03805 & 0.03778 \\ \(p\)-value of Coverage & 0.00272 & 0.00278 \\ \(p\)-value of Average precision & 0.04256 & 0.04231 \\ \(p\)-value of Ranking loss & 0.03805 & 0.04121 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Wilcoxon test results comparing the performance of different encoding methods for subcellular localization prediction. performance improved significantly. The attention mechanisms allowed the model to focus on the most relevant parts of the sequence, enhancing both predictive power and interpretability. By combining Bi-LSTM's sequence modeling capabilities with attention mechanisms, CSpredR effectively leverages the strengths of both approaches. Finally, CSpredR demonstrated the highest performance, combining the advantages of both CNN and Bi-LSTM.