## 3 Results and Discussion
### Analysis of Model Prediction Results  
To further study and analyze the prediction effect of our model, the MMSNet algorithm was employed to predict the function of proteins that were not included in the training dataset. The predicted protein data were selected from the experimental prediction results of three sub-ontology datasets of proteins (MF, BP, and CC). The comparison results for the  
Figure 6: Performance comparison of MMSNet and the other two algorithms on the ontology dataset. protein functions selected in this section are shown in Table 4. We focused on analyzing the functions of some japonica proteins based on the latest research papers and model prediction results. We want to emphasize that we were not only limited to the study of model performance, but also paid more attention to the practical significance of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grain** & \begin{tabular}{c} **Sub-** \\ **Ontology** \\ \end{tabular} & **Protein** & \
\begin{tabular}{c} **Real** \\ **Function** \\ \end{tabular} &
\begin{tabular}{c} **Predicted** \\ **Function** \\ \end{tabular} \\ \hline MF & & & GO:0046872 & GO:0046872 \\  & P16081 (NIA1\_ORYS) & GO:1901363 & GO:0043169 \\  & & GO:0043167 & GO:0043167 \\  & & & GO:0045848 & GO:0045848 \\  & & & GO:0016491 & GO:016491 \\  & & & GO:0003824 & GO:0003824 \\ \cline{3-5}  & MF & & GO:0003824 & GO:0003824 \\  & P16081 (NIA1\_ORYS) & GO:0005488 & GO:0005488 \\  & & & GO:1901363 & GO:1901363 \\  & & & GO:0097159 & GO:0097159 \\  & & & GO:0003676 & GO:0003676 \\  & & & GO:0043168 & GO:0003723 \\  & & & GO:0016787 \\ \cline{3-5}  & & & GO:0003677 & GO:0003677 \\  & & & GO:0003676 & GO:0003676 \\  & & QQJK9 (ARFB\_ORYS) & GO:00097159 & GO:0007159 \\  & & & GO:1901363 & GO:1901363 \\  & & & GO:0005488 & GO:0005488 \\ \cline{3-5} Japonica & & & GO:0008152 \\  & & & GO:0044238 & GO:0008152 \\  & & & GO:0005975 & GO:0044238 \\ \cline{3-5} BP & & & GO:0005975 & GO:0005975 \\  & & & GO:0005975 & GO:0044238 \\  & & & GO:00044238 & GO:0008152 \\  & & & GO:0008152 & GO:00005805 \\  & & & GO:00061601 \\ \hline  & & & GO:0009507 & GO:0009507 \\  & & & GO:0009536 & GO:0009536 \\  & & & GO:0043231 & GO:0043231 \\  & & & GO:0043227 & GO:0043227 \\  & & & GO:0043229 & GO:0043229 \\  & & & GO:0043226 & GO:0043226 \\ CC & & & GO:0110165 & GO:0110165 \\  & & & GO:0016020 \\ \cline{3-5}  & & QGYSJ5 (AGO16\_ORYS) & GO:0005737 & GO:0005737 \\  & & & GO:0110165 & GO:0110165 \\  & & & GO:0005886 & GO:0016020 \\  & & & GO:0016020 \\  & & & GO:0110165 & GO:0110165 \\ \hline  & & & GO:0005886 \\  & & & GO:0016020 \\  & & & GO:0110165 & GO:0110165 \\ \cline{3-5}  & MF & & A2Y9M4 (SSY1\_ORYS) & GO:0003824 \\  & & & GO:0016740 & GO:0003824 \\  & & & GO:0003735 \\  & & & GO:0005198 \\  & & & GO:0005488 \\ \cline{3-5}  & & & GO:0009987 \\  & & & GO:0008152 \\ Indica & BP & & GO:0044238 \\  & & & GO:0071704 & GO:0071704 \\  & & & GO:0008152 \\ \cline{3-5}  & & & GO:00032991 \\  & & & GO:0110165 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of protein function prediction results for four types of grains. For some protein samples, all the preset labels were correctly predicted by the model, demonstrating excellent prediction accuracy. For example, in the MF sub-dataset, the P29250 (LOX2_ORYS) protein was annotated as GO:0046872, GO:0043169, GO:0043167, GO:0005488, GO:0016491, and GO:0003824. The functions predicted by the model were consistent with actual annotations. The P29250 protein was also annotated with the GO:0046872 function in the UniProt database, as inferred from electronic annotation. Furthermore, [PERSON] indicated that the P29250 protein might play a key role in biological processes related to metal ion binding (GO:0046872) [30]. The Q0 JKI9 (ARFB_ORYS) proteins in the test set were annotated using GO:0003677, GO:0003676, GO:0097159, GO:1901363, and GO:0005488. The function of the Q0 JKI9 protein was accurately predicted by the model and aligned with its annotated function.