## 3 Results
### Selection of Characteristic Genes from Machine Learning (ML) and Meta-Analysis  
We applied the SHAP technique to interpret the predictions of the selected top three ML models (Figure 5a-c), as SHAP values can demonstrate the positive or negative impact of a feature variable on the outcome prediction. We first selected the top 15 genes from each model based on mean absolute SHAP values and compared them to find consistently prioritized genes by all three methods. Notably, _NETO2_, _PAX6_, _BCL11B_, _GFAP_, _GABRD_, and _PHACTR1_ repeatedly appeared among the top predictors (Figure 5d), exhibiting robust and consistent association with the disease classification. To further investigate how different expression levels of genes contributed positively or negatively to the probability of HD, we computed a SHAP summary plot for each model, presenting the relationship between  
Figure 4: Performance assessment of 19 machine learning (ML) classifiers and selection of the top three classifiers for biomarker selection. **(a)** Heatmap showing the performance of 19 ML classifiers on the train and test sets, where each row represents a classifier and each column corresponds to a metric. Color intensity ranging from 0 to 1 (blue to red) reflects the relative performance of each model. **(b)** Confusion matrices of the top-performing models showcased how accurately each model classified [PERSON]’s disease (HD) and control by comparing the model’s predicted and actual labels. **(c,d)** The receiver operating characteristic (ROC) curve of the top-performing classifier on the train and test datasets shows its ability to distinguish between HD and Control.  
the expression levels of genes and the degree of high and low SHAP values in the testing dataset. On the SHAP summary plot, feature variables with higher SHAP values were closely associated with an increased risk of developing HD. In the top-performing model ET (Figure 5a), we found that _PAX6_ shows high feature values x (red dots) clustered on the left side, where SHAP values are negative, which means that the high expression of _PAX6_ is associated with a lower predicted probability of HD occurring. Conversely, low expression (blue points) corresponded to higher SHAP values and thus increased the model's predicted probability of HD occurrence. This is seen by other top-performing models (CB, XGB) (Figure 5b,c). The _GFAP_ gene, which is at the top of the XGB model (Figure 5c), shows that high expression levels (red dots) are mainly found in the negative SHAP region, indicating that increased GFAP expression is associated with reducing the risk of HD occurring. Conversely, the feature _BCL11B_ shows the opposite impact, where the high expression value is on the positive side of the SHAP region, leading to a significant increase in expression, significantly increasing the model's prediction of HD occurrence. Other genes follow similar patterns. The high expression value (red points) of _GABRD_ was correlated with positive SHAP values, signifying that an increase in _GABRD_ expression positively influences the model's prediction of HD. In contrast, lower _GABRD_ expression (blue spots) correlated with negative SHAP values, indicating a reduced risk. A similar expression pattern was also observed for _PHACTR1_. Subsequently, we selected the five robust genes that were found to be present by both ML and meta-analysis with stringent criteria (Figure 5e), and further visualized their contribution to model output using SHAP dependence plots to explore feature interactions and effect directions (Figures S3-S5).