Hypothesis

We hypothesize that a transformer‑based survival model pre‑trained on large, heterogeneous biobank cohorts (e.g., UK Biobank) and subsequently fine‑tuned on specialized longevity datasets will capture time‑lagged, multi‑omic risk patterns that precede changes in hazard by months to years, and that pathway‑constrained attention layers will improve both predictive performance and biological interpretability over standard transformer survival models.

Mechanistic Rationale

1. Transfer Learning Mitigates Small‑Sample Overfitting – Aging cohorts often have limited event counts, making complex models prone to overfit. Pre‑training on millions of records from biobanks provides a rich prior over baseline hazard shapes and covariate interactions, which fine‑tuning can adapt to longevity‑specific genetics and lifestyle factors [4].
2. Pathway‑Aware Attention Encodes Biological Priors – By restricting the self‑attention matrix to genes within curated pathways (e.g., cGAS‑STING, mTOR, senescence‑associated secretory phenotype), the model forces each token to interact primarily with biologically plausible partners. This reduces spurious high‑dimensional correlations and yields attention weights that map directly onto known aging mechanisms [5].
3. Time‑Lagged Multi‑omic Fusion via Temporal Convolutional Blocks – Molecular alterations (transcriptomic, proteomic, metabolomic) often occur before clinical events. We propose inserting a dilated temporal convolutional block before the transformer encoder, allowing the model to learn latent representations of omic histories at multiple lag windows (0‑6, 6‑12, 12‑24 months). The transformer then attends over these lag‑encoded summaries, explicitly modeling delayed effects that instantaneous Cox or DeepSurv formulations ignore [3].
4. Censoring‑Aware Contrastive Loss – Standard negative log‑likelihood does not fully exploit censored samples. We will augment the loss with a contrastive term that pulls together feature vectors of subjects with similar survival times while pushing apart those with divergent hazards, weighted by the inverse probability of censoring. This should improve calibration in heavily censored longevity cohorts [2].

Testable Predictions

• Prediction 1: The transfer‑learned, pathway‑constrained model will achieve a C‑index ≥0.85 on an external longevity validation set (e.g., Longevity MAP), surpassing the 0.79‑0.81 range reported for vanilla transformer survival models on medical data [1][2].
• Prediction 2: Attention weights aggregated across pathways will show significant enrichment (FDR < 0.05) for known aging hallmarks (e.g., IFN‑γ response, mitochondrial dysfunction) compared to random pathway shuffles.
• Prediction 3: Introducing lagged omic blocks will increase the C‑index by at least 0.03 points relative to a baseline model that concatenates contemporaneous omics, demonstrating that temporal precedence improves risk stratification.
• Prediction 4: Ablation of the contrastive censoring term will lead to a measurable drop in calibration (Hosmer‑Lemeshow p‑value <0.01) without affecting discrimination, confirming its utility for censored data.

Experimental Design

1. Data: Pre‑train on UK Biobank (n ≈ 500 k) with baseline omics and electronic health records; fine‑tune on the Longevity MAP cohort (n ≈ 2 k) with longitudinal transcriptomics, proteomics, metabolomics, and mortality follow‑up.
2. Model Architecture: Dilated TCM (kernel sizes 2, 4, 8) → transformer encoder with masked self‑attention restricted to pathway adjacency matrices → hazard head via piecewise exponential output.
3. Evaluation: Time‑dependent C‑index, integrated Brier score, and calibration plots across 1‑, 3‑, and 5‑year horizons; SHAP pathway attribution to validate mechanistic relevance.
4. Falsification: If the transfer‑learned model does not outperform the vanilla transformer survival model by the stipulated margins, or if pathway attention fails to enrich aging signatures, the hypothesis is refuted.

Implications

Confirming this hypothesis would establish a principled framework for leveraging massive, heterogeneous health resources to predict biological age, illuminate delayed molecular cascades that drive mortality, and provide interpretable biomarkers for intervention trials in aging research.