Hypothesis

Integrating transformer-based feature learning with Bayesian uncertainty quantification and time-varying effect modeling yields a survival model (BT-TVEM) that significantly outperforms current benchmarks for disability-free survival and identifies epigenetically driven, age‑dependent pathways of frailty.

Model Architecture

• Transformer Encoder: Processes raw DNA‑methylation β‑values (or proteomic intensities) across CpG sites, learning non‑linear interactions and producing a latent representation Z.
• Attention‑Weighted Pathway Mapping: The self‑attention matrices are aggregated to assign importance scores to each CpG; these scores are projected onto curated pathway databases (e.g., KEGG, Reactome) to generate a dynamic pathway activity vector P(t) that varies with age.
• Bayesian Survival Layer: A Cox‑type hazard function λ(t|Z,P) = λ₀(t) exp(βᵀZ + γ(t)ᵀP) where β are fixed effects and γ(t) are time‑varying coefficients modeled via Bayesian penalized splines, providing posterior predictive distributions for individual hazards.
• Training Objective: Maximize the marginal likelihood of observed survival times while regularizing attention sparsity and spline smoothness.

Testable Predictions

1. Predictive Performance: In the Framingham Heart Study offspring cohort (n≈3,000, 12‑year follow‑up), BT-TVEM will achieve a concordance index (C‑index) ≥0.86 for disability‑free survival, exceeding the Healthy Longevity Index (C‑index 0.79) and the TTSurv baseline (≈0.81) [3, 1].
2. Uncertainty Calibration: The 95% credible intervals from BT‑TVEM will show better calibration (lower Brier score) than point‑estimate confidence intervals from DeepSurv, demonstrating improved quantification of individual risk [2].
3. Mechanistic Biomarkers: CpG sites receiving the top 5 % attention weights will be significantly enriched (FDR < 0.05) for NF‑κB signaling and interferon‑response pathways, and their time‑varying coefficients γ(t) will exhibit a positive acceleration after age 70, correlating with rising IL‑6 levels and frailty index scores.
4. Generalizability: When applied to an independent longitudinal proteomics dataset (e.g., UK Biobank Pharma Proteomics Project), BT‑TVEM will retain a C‑index ≥0.83, indicating that the transformer‑learned latent space captures conserved aging signals across omics layers.

Mechanistic Insight Beyond Existing Work

While prior transformer survival models (e.g., TTSurv) improve feature extraction [1] and Bayesian neural Cox models quantify uncertainty [2], they treat hazard coefficients as static or rely on pre‑selected features. BT-TVEM introduces two novel mechanisms:

• Dynamic Pathway Mediation: By converting attention weights into time‑varying pathway activities P(t), the model directly links molecular network re‑wiring to hazard changes, offering a causal‑like intermediate that can be validated with longitudinal cytokine measurements.
• Sparse Bayesian Splines for γ(t): Modeling γ(t) with Bayesian penalized splines allows the data to dictate when and how strongly a pathway influences mortality, capturing non‑proportional hazards without over‑fitting—a feature absent in standard TVEM or piecewise exponential approaches [4, 5]. This creates a closed loop: transformer learns complex CpG interactions → attention highlights pathways → Bayesian splines quantify their age‑specific impact → posterior hazards inform individualized healthspan forecasts.

Falsifiability

The hypothesis is falsifiable if any of the following occur:

• BT‑TVEM’s C‑index fails to exceed 0.82 on the primary endpoint, indicating no meaningful gain over existing deep learning survival models.
• Posterior credible intervals show no improvement in calibration or sharpness relative to frequentist intervals from Cox‑PH with splines.
• Top‑attention CpGs are not enriched for inflammatory or stress‑response pathways, or their γ(t) trajectories do not align with known frailty biomarkers.
• Performance drops substantially (<0.75 C‑index) when tested on an external omics cohort, suggesting lack of generalizability.

Conclusion

By uniting transformer representation learning, Bayesian uncertainty, and time‑varying effect modeling within a unified survival framework, BT‑TVEM offers a testable, mechanistically grounded avenue to push healthspan prediction beyond the current 0.79 C‑index ceiling while unveiling the epigenetic circuitry that drives late‑life disability.