## 5 Experimental Evaluation  
Experimental results show that applying LoRA tuning to BiomedBERT yields a strong gain in polarity classification without the heavy cost of full model retraining. After eight training epochs, the fine-tuned model correctly classified 2347 out of 2936 test samples--an accuracy of roughly 79.96%. Figure 2 provides a multi-angle comparison. In Figure 2a,b, the confusion matrices show that while the baseline model struggles with both classes, LoRA recovers a more balanced and accurate classification. Figure 2c tracks clear gains across accuracy, F1, precision, and recall. Figure 2d further confirms that LoRA improves both synergistic and antagonistic recognition. ROC curves in Figure 2e show significant improvement in area under the curve (AUC), rising from 0.449/0.644 (baseline) to 0.864/0.866 (LoRA). This demonstrates how well the model balances precision and recall when it finds synergistic interactions, which were the positive class during evaluation. The model stays very confident across a wide range of thresholds, with an area under the curve of 0.865. In real life, this is useful because decisions often depend on being able to tell the difference between combinations that are reliably good and those that are neutral or bad. However, since precision-recall (PR) curves are asymmetric, a complete view of model performance requires plotting an additional curve using _Synergistic_ as the positive class. Figure 2: Comparison between the pretrained BiomedBERT and the LoRA fine-tuned BiomedBERT on the test set. (**a,b**) Confusion matrices show LoRA’s improved classification across both classes. (**c**) Metric-wise comparison reveals substantial accuracy and F1 improvements post fine-tuning. (**d**) Per-class F1 scores highlight more balanced performance under LoRA. (**e**) ROC curves show AUC gains for both synergy (0.864) and antagonism (0.866) under LoRA. (**f**) Precision–recall curves indicate stronger class separation and confidence consistency in the LoRA model. This is achieved by inverting the class labels and their associated probabilities. The multi-curve evaluation in Figure 2f highlights recall-precision trade-offs between classes, exposes mild imbalance, and reveals asymmetries that would be hidden in scalar metrics like F1 or accuracy. In clinical applications, a higher PR curve is desirable when false positives (e.g., predicting synergy when not present) would lead to risky combinations. Therefore, this analysis helps prioritize recall or precision depending on the clinical tolerance for risk. We trained and tested the model in both the synergistic and antagonistic polarity classes, using balanced data. Figure 3 shows the features of the data set, such as the class distribution, how the data set is divided up, and the lengths of the input sentences. The accuracy, precision, recall, F1 score, and confusion matrix all show how well both classes did. During training and analysis, no class was left out. To obtain a point of reference, we also ran the base BiomedBERT model without any fine-tuning. We used the same test set and did not apply LoRA or any task-specific adjustments. The results were clearly weaker, especially for synergy cases, where the model often missed the correct label; see Table 3. This makes sense, since the original model was not trained to recognize polarity. These baseline scores helped us gauge how much the tuning process actually improves the outcome. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **Pretrained** & **LoRA** & **Absolute Diff** & **Relative Diff (\%)** \\ \hline Accuracy & 0.4986 & 0.7994 & 0.3007 & 60.31 \\ F1 & 0.3339 & 0.7975 & 0.4636 & 138.82 \\ Precision & 0.3747 & 0.8109 & 0.4363 & 116.45 \\ Recall & 0.4986 & 0.7994 & 0.3007 & 60.31 \\ F1 Synergistic & 0.0027 & 0.8170 & 0.8143 & 30,048.18 \\ F1 Antagonistic & 0.6652 & 0.7780 & 0.1128 & 16.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test set performance comparison between pretrained BiomedBERT and BiomedBERT with LoRA fine-tuning models.