Interpretable integration of unpaired multi-omics for Alzheimer’s diagnosis via cross-modal transformer reconstruction
Fig 2
Performance and confusion matrices of AE-Trans and comparative algorithms.
Fig 2. Comprehensive performance evaluation and generalizability analysis of AE-Trans. The model was benchmarked against multiple baseline methods across internal and external cohorts. (A) Comparison of Accuracy. Bar charts representing the classification accuracy of AE-Trans versus six comparative algorithms, demonstrating consistent superiority. (B) Comparison of AUC scores. Quantitative comparison of Area Under the Curve (AUC) values, highlighting the robust discriminative power of the proposed framework. (C) ROC curve analysis. Receiver Operating Characteristic (ROC) curves illustrating the diagnostic performance across different validation sets. (D) Cross-region external validation. Confusion matrix showing the classification results on External test 1, which comprises unpaired samples from geographically distinct regions. (E) Multi-scenario performance metrics. Summary of key performance indicators across three external validation scenarios, including cross-region, same-region, and paired datasets. (F) Same-region external validation. Confusion matrix displaying the model’s predictive accuracy on External test 2, consisting of unpaired samples from the same geographic region.