Interpretable integration of unpaired multi-omics for Alzheimer’s diagnosis via cross-modal transformer reconstruction
Fig 4
Comparative Performance Analysis of Logistic Regression Using Selected Important Features Versus Alternative Feature Selection Methods.
This Fig demonstrates the superior diagnostic power of AE-Trans-prioritized features and their generalizability across different machine learning backbones. (A–B) Benchmark of feature selection methods. ROC curves (A) and quantitative comparison (B) of logistic regression (LR) classifiers built using features selected via AE-Trans (top 200), F-score, CV², PCA, and random selection. (C–E) Comparison with statistical biomarkers. Performance metrics and ROC analysis comparing AE-Trans features against traditional differentially expressed genes (DEGs) and methylated sites (DMSs), highlighting the advantage of nonlinear feature integration. (F) Feature ablation study on AE-Trans. Stepwise removal of top-ranked features (Top 1–20, 21–50, 51–100) illustrates the concentration of predictive information within the highest-scoring biomarkers. (G) Generalizability of AE-Trans explainability. Performance improvement (Accuracy, Precision, Recall, F1, AUC) of baseline models—DeepBelief, DEG-DMP-DNN, and LR—after incorporating AE-Trans-selected features. (H) AUC enhancement across architectures. Comparison of AUC scores for various models before and after applying AE-Trans-based feature selection, showing marked improvements in deep learning and linear backbones. (I) Feature ablation on LR backbone. Validation of feature importance using a logistic regression model, where the removal of methylation and key omics features leads to significant performance degradation.