Interpretable integration of unpaired multi-omics for Alzheimer’s diagnosis via cross-modal transformer reconstruction
Fig 7
Comparison of prediction performance between multi-dimensional fusion and single dimensional data.
This Fig quantifies the predictive gains achieved by integrating RNA-seq and DNA methylation data compared to using individual omics channels. (A, C) Performance benchmarking in five-fold cross-validation. Comparison of Accuracy, Precision, Recall, F1-measure, and AUC across different input configurations, showing that the dual-channel AE-Trans model (AUC = 0.9883) consistently outperforms single-modality approaches during training. (B, D) Validation on the independent test set. Results demonstrate the superior generalizability of the multi-omics fusion strategy (Accuracy = 0.9736; AUC = 0.9910) over RNA-only (Accuracy = 0.9357) and methylation-only models.