## 3 Results
### Construction of a Nomogram and Validation by External Datasets  
Next, we constructed a nomogram using the training dataset of ML to predict the potential risks of developing HD and assess the predictive capacity of five biomarkers identified by ML models, including _PAX6, GFAP, BCL11B, PHACTR1_, and _GABRD_. As depicted in Figure 6a, the nomogram model assigns total points ranging from 0 to 240, where a point from 142 to 212 corresponds to 0.1 to 0.9 of the risk of developing HD. The calibration curve was constructed using bootstrapping (resampling = 1000). As illustrated in Figure 6b, both the apparent and bias-corrected curves closely align with the _45-degree_ ideal line, exhibiting a mean absolute error of 0.035, which indicates that the model's predicted probability aligns well with the observed occurrence rates in the training cohort. Subsequently, the nomogram was evaluated on two different external datasets (GSE33000, GSE9857). The validation curve of GSE33000 (Figure 6c) demonstrates strong discrimination ability of the predictive model, attaining a C-statistic of 0.948. The probability estimates of the model were highly reliable, with a mean absolute calibration error (Eavg) of 0.053 and a Brier score of 0.090, further indicating well-calibrated and accurate risk predictions. The ROC analysis (Figure 6d) conducted on the external validation cohort resulted in an AUC of 0.948, consequently affirming the model's robust discriminative capability. Decision curve analysis performed in both the training and external validation cohorts (Figure 6e) indicates that the nomogram offers significant net benefit across various clinically relevant threshold probabilities, highlighting its potential applicability in practice. Similarly, on the external dataset GSE9857, the model achieved an AUC of 0.938, the apparent and bias-corrected calibration curves closely tracked the 45\({}^{\circ}\) ideal line (mean absolute error = 0.071.; Brier score = 0.106), and decision curve analysis demonstrated net clinical benefit across a wide range of threshold probabilities, mirroring the findings in the human datasets (Figure S6). Figure 5: Identification of five robust biomarkers through the integration of machine learning (ML) and differentially expressed gene (DEG) meta-analysis. (**a**–**c**) SHAP summary plots highlighting the significance of the top 20 genes for three top-performing ML classifiers: (**a**) ExtraTrees (ET), (**b**) CatBoost (CB), and (**c**) ExtremeGradientBoosting (XGB). The SHAP summary plot illustrates the contribution from each gene to the model prediction for [PERSON]’s disease (HD). The right panel (beeswarm plot) displays individual gene expression contributions, where each dot represents a sample and its corresponding SHAP value. The color gradient indicates gene expression levels, with red denoting high expression and blue indicating low expression. The left panel (bar plot) presents the average absolute SHAP value for each gene, summarizing its overall impact across all samples. The plot showcases the contribution from each gene to the model prediction for HD, where each dot represents a single gene expression value. While the red color represents high expression, the blue color represents low. (**d**) Six genes were consistently ranked among the top 15 features across all three models in SHAP plots across all three classifiers. (**e**) Five genes were selected as robust biomarkers as they overlapped with the candidate genes identified by ML and the DEGs found in meta-analysis using strict criteria. To evaluate the discriminative ability to distinguish between HD and control samples, ROC curve analysis of the five genes was conducted. The outcome shown in Figure S7 demonstrates that, except for _GFAP_, four genes, including _PAX6_, _BCL11B_, _GABRD_, and _PHACTR1_, display an AUC higher than 0.80 in the external human dataset (GSE3000), whereas, in the external mouse cohort, all the genes display an AUC higher than 0.60 (Figure S8).