All models were trained using the AdamW optimizer , weighted binary cross-entropy loss, and batch size 32. The positive class weight was set to 2.8 to achieve the best possible balance between precision and recall, thus maximizing the MCC, our primary evaluation metric. We searched for the optimal values of n, m, and s with the Optuna library through 10-fold cross-validation, assessing each configuration's performance as the average across all 10 validation folds. The initial learning rate was reduced by a factor of 10 after 10 epochs without improvement of the validation loss, and early stopping was applied after 20 epochs without improvement. The number of Optuna trials was set to 20. The hyperparameters and 10-fold CV performance of all models can be found in Table .