## 3 Results and Discussion
### Comparison with Other Single Label Multi-Class Classification Methods  
To verify the superior performance of the CSpredR model, we made key adjustments to the classification part while ensuring that the feature extraction and model construction mechanisms of the CSpredR model remained unchanged. Specifically, we changed the originally designed multi-label classification task model structure to a single-label multi-class classification task. This modification allowed us to directly compare the CSpredR model with existing single-label multi-class models. By using the single-label multi-class task as an evaluation metric, we could more clearly understand the performance of the CSpredR model in different application scenarios and directly compare it with other popular multi-label classification models. For this purpose, we constructed a single-label classification benchmark dataset by selecting 12,884 mRNA sequences from the 36,971 entries derived after preprocessing the initial dataset. These sequences were further refined to ensure consistency and suitability for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \
\begin{tabular}{c} **Hamming** \\ **Loss** \\ \end{tabular} & **One-Error** & **ACC** & **Coverage** &
\begin{tabular}{c} **Average** \\ **Precision** \\ \end{tabular} & **Ranking Loss** \\ \hline CNN & 0.322 & 0.710 & 0.437 & 7.496 & 0.496 & 0.517 \\ Bi-LSTM & 0.289 & 0.656 & 0.512 & 6.942 & 0.530 & 0.471 \\ CNN + attention & 0.276 & 0.671 & 0.525 & 6.828 & 0.579 & 0.429 \\ Bi-LSTM + attention & 0.235 & 0.632 & 0.578 & 6.440 & 0.646 & 0.402 \\ CSpredR & 0.182 & 0.605 & 0.657 & 6.035 & 0.675 & 0.380 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experimental results based on different network model structures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CNN** & **Bi-LSTM** & \
\begin{tabular}{c} **CNN+** \\ **Attention** \\ \end{tabular} &
\begin{tabular}{c} **Bi-LSTM+** \\ **Attention** \\ \end{tabular} \\ \hline \(p\)-value of Hamming loss & 0.01354 & 0.01964 & 0.01978 & 0.02169 \\ \(p\)-value of One-error & 0.03141 & 0.03783 & 0.04918 & 0.04571 \\ \(p\)-value of ACC & 0.03192 & 0.03794 & 0.04126 & 0.04780 \\ \(p\)-value of Coverage & 0.00429 & 0.01468 & 0.02779 & 0.03014 \\ \(p\)-value of Average & 0.03837 & 0.03994 & 0.04108 & 0.04296 \\ precision & 0.02711 & 0.02846 & 0.02971 & 0.03249 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wilcoxon test results for performance comparison across different network structures in subcellular localization prediction. single-label classification tasks. Using a 5-fold cross-validation setup, we trained the model on four subsets and tested it on the remaining one, ensuring a robust and reliable evaluation. This helped us to comprehensively evaluate the applicability and performance differences in the CSpredR model in single-label, multi-label, and various task contexts. We used accuracy, precision, recall, and F1 score as evaluation metrics to assess the classification performance, prediction accuracy, and performance in multi-class problems of the CSpredR model. Table 6 shows the comparison of CSpredR based on an independent test set with other predictors. The results indicate that the CSpredR model performed excellently in terms of accuracy, reaching 0.671, surpassing other predictors. Its precision was 0.755, recall was 0.592, and F1 score was 0.643, also showing significant advantages in multi-class problems. This demonstrates that the CSpredR model not only has a competitive edge in prediction accuracy but also exhibits excellent performance in handling multi-class problems. This paper further compares the performance of CSpredR in single-label prediction with various advanced methods, all of which are accessible via web servers, including iLoc-mRNA, mRNALoc, mRNALocator, and DM3 loc. To ensure fairness in the comparison, mRNA sequences from the independent test set were submitted to each server to obtain their predicted subcellular localization results, which were then compared with the true labels.