## 6 Discussion  
This study evaluated the effectiveness of our method in predicting the responses of bladder cancer patients to neoadjuvant therapy using transcriptomic data. The Boruta algorithm used in this study initially identified genes linked to treatment outcomes. Our Boruta-80 model outperformed other versions, highlighting the importance of choosing \begin{table}
\begin{tabular}{l c c c c c} \hline
**Network Size** & **AUC-ROC** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline Boruta-70 & 0.8346 \(\pm\) 0.0278 & 0.6789 \(\pm\) 0.0706 & 0.7619 \(\pm\) 0.0892 & 0.8000 \(\pm\) 0.2266 & 0.7693 \(\pm\) 0.0778 \\
**Boruta-80** & **0.8724 \(\pm\) 0.0511** & **0.7789 \(\pm\) 0.068** & **0.8529 \(\pm\) 0.0338** & **0.9231 \(\pm\) 0.0719** & **0.8038 \(\pm\) 0.0955** \\ Boruta-90 & 0.8256 \(\pm\) 0.0262 & 0.7526 \(\pm\) 0.0235 & 0.831 \(\pm\) 0.0266 & 0.9000 \(\pm\) 0.1108 & 0.7827 \(\pm\) 0.0557 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of models with different network sizes. Figure 4: Ablation study results showing the impact of different head counts and channel numbers on model performance. relevant genes for increased model precision. This aligns with Occam's Razor, suggesting that simpler models often generalize better while maintaining strong predictive power. However, in the field of biomedical research, considering the complexity of biological processes, the complexity of models is sometimes necessary [47]. We underscore the importance of integrating biological insights with network-based ML techniques in clinical decision-making for neoadjuvant therapies. A major challenge we faced was applying traditional machine-learning methods to complex genetic data, which often led to issues of overfitting or underfitting [48; 49]. In our experiment, despite SVM exhibiting commendable precision (\(0.936\pm 0.0615\)), it may fail to accurately identify all patients who could benefit from the treatment, therefore increasing the risk of misdiagnosis. This concern is further demonstrated by the low recall rate of SVM (\(0.5461\pm 0.3021\)), reflecting the potential for overlooking patients who would respond positively to the therapy. Additionally, the clinical application of artificial intelligence in the medical field faces challenges in terms of transparency and reproducibility [50]. Studies in the domain of radiomics machine-learning (employing methods to analyze CT and MRI data for disease prediction, akin to our focus on small, high-dimensional datasets for clinical prognosis) have shown that single random train-test splits may lead to unreliable model performance assessments [51]. This highlights the importance of employing cross-validation or other techniques for a more accurate evaluation of model generalization capabilities. Against this backdrop, our proposed MVMSGAT model demonstrates more promising results compared to traditional models. Across multiple performance metrics, including AUC-ROC (\(0.8724\pm 0.0511\)), accuracy (\(0.7789\pm 0.068\)), F1 score (\(0.8529\pm 0.0338\)), recall (\(0.9231\pm 0.0719\)), and precision (\(0.8038\pm 0.0955\)), MVMSGAT generally outperforms SVM, DT, RF, GBDT, GAT, MSGAT. Incorporating biological prior knowledge provides a significant benefit: models such as GAT, MSGAT, and MVMSGAT demonstrate smaller standard deviations in five-fold cross-validation, suggesting more stable results. This stability, particularly in contrast to traditional data-driven machine-learning methods, is likely because graph-based models handle high-dimensional data more effectively and are better at capturing complex biomarker interactions. These interactions, such as gene-gene interactions or protein pathways, are crucial for understanding disease mechanisms. Instead of relying solely on data-driven patterns like traditional methods, these models integrate biological insights, potentially enhancing their analytical capabilities and yielding more reliable and consistent results [52].