Interpretable integration of unpaired multi-omics for Alzheimer’s diagnosis via cross-modal transformer reconstruction
Fig 6
AE-Trans Achieves High-Accuracy Classification and Survival Risk Stratification in RNA-Only Alzheimer’s Disease Data.
AE-Trans was evaluated for its diagnostic generalizability and its ability to uncover clinically relevant biological heterogeneity using GSE118553 and GSE29378. (A, E) Diagnostic performance comparison. ROC curves for GSE118553 (A) and GSE29378 (E) show that classifiers fine-tuned on AE-Trans latent representations significantly outperform those trained on raw RNA data. (B, F) Latent space visualization. UMAP projections of the latent embeddings for GSE118553 (B) and GSE29378 (F), illustrating a clear separation between AD and control samples, while identifying a subset of AD cases with control-like molecular profiles. (C, G) Discovery of AD molecular subtypes. Unsupervised clustering of latent vectors within AD patient cohorts identifies two distinct molecular subtypes in both GSE118553 (C) and GSE29378 (G). (D, H) Clinical prognostic relevance. Kaplan–Meier survival analysis demonstrates significantly different clinical outcomes between the identified AD subtypes, confirming that AE-Trans captures prognostically meaningful biological heterogeneity even from single-modality inputs.