Fig 1.
The architecture comprises modality-specific processing, cross-modal alignment, and downstream interpretability modules. (A) Data pairing strategy. Unpaired RNA and DNA methylation samples are integrated through a combinatorial intra-label pairing scheme based on diagnostic status (AD vs. Control). (B) Dimensionality reduction and encoding. High-dimensional inputs are compressed via modality-specific autoencoders, followed by Transformer encoders with multi-head attention to capture global dependencies. (C) Fusion and classification. Latent embeddings from dual channels are concatenated and passed through a linear fusion layer to a multilayer perceptron (MLP) for AD probability prediction. (D) Dual-path reconstruction. A bidirectional reconstruction unit aligns cross-modal representations using cycle-consistency loss, ensuring the preservation of biological signals across transcriptomic and epigenomic spaces. (E) Counterfactual attribution. Model interpretability is achieved via Counterfactual Integrated Gradients (CIG), quantifying the contribution of individual features to identify AD-related biomarkers. (F) Clinical validation. Latent representations enable patient stratification into subgroups with distinct survival outcomes and transcriptomic profiles, demonstrating the model’s prognostic utility.
Table 1.
Performance Comparison of AE-Trans with Other Methods.
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.
Table 2.
Model Performance on Cross-Region, Same-Region, and Paired External Test Datasets.
Fig 3.
Interpretable AE-Trans Analysis via Counterfactual Integrated Gradients with Key Feature Highlight.
The model utilizes Integrated Gradients (IG) to attribute predictive outputs to specific multi-omics features. (A-B) Global attribution profiles. Density plots showing the highly skewed distribution of IG scores for RNA (A) and DNA methylation (B) features, where the majority of predictive weight is concentrated in a small subset of features. (C) Quantitative ranking of top features. Bar charts illustrating the top 20 biomarkers prioritized by their average attribution scores and ranking stability across five-fold cross-validation. (D) Statistical significance of top biomarkers. Comparison of expression and methylation levels between AD and Control groups for the top-ranked features, with significance determined by Wilcoxon rank-sum tests and Benjamini-Hochberg FDR correction. (E) Heatmap of candidate biomarkers. Visualization of differential expression and methylation patterns for the identified top features, showing clear group separation and consistent biological signals across AD and non-AD samples.
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.
Fig 5.
Functional Enrichment and Co-expression Network Analysis Based on Important Features.
This Fig characterizes the pathophysiological relevance and systemic interactions of the top-ranked multi-omics features. (A–B) Pathway enrichment analysis. Results from Gene Ontology (GO) and KEGG enrichment analysis on the top 100 features, highlighting key AD-related processes including immune activation (T cell receptor signaling), metabolic dysregulation (glucose metabolism), and synaptic regulation (axon guidance). (C) Co-expression module analysis. Pearson correlation network (r > 0.75) of the top 20 RNA features revealing two distinct functional modules: Module 1 (e.g., NCK2, MEF2C) associated with synaptic plasticity, and Module 2 (e.g., TBC1D1, MLKL, MS4A7) linking metabolism, necroptosis, and neuroinflammation. (D) Network topology and hub gene identification. Visualization of the molecular interaction landscape where genes such as NCK2 and TBC1D1 emerge as central hubs, suggesting their roles as master regulators in the coordination of AD-relevant molecular programs.
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.
Table 3.
Performance evaluation table for different omics data.
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.
Table 4.
Model ablation performance evaluation table.