Hypothesis

Integrating latent confounder variables directly into equivariant graph neural network (eqGNN) training via adversarial confounder encoding will enable the model to learn representations that isolate nonconfoundable network substructures—topological motifs that persist across batches, tissues, and species—and thereby prioritize aging‑specific drug targets with higher causal validity than current pretrained eqGNNs.

Mechanistic Rationale

Recent eqGNNs achieve AUROC 0.9665 by incorporating 3D protein structure and self‑supervised pre‑training on unlabeled molecules【https://academic.oup.com/bib/article/26/5/bbaf554/8303310】【https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1452339/full】. However, these models still assume that the observed graph is free of hidden confounders【https://pmc.ncbi.nlm.nih.gov/articles/PMC11494969/】. When hidden variables such as batch effects, cell‑type composition, or genetic ancestry correlate with both topology and labels, the network learns spurious edges that masquerade as true interactions【https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac014/6547681】. Causal work shows that standard confounder removal via GLM before training can degrade performance to below‑chance levels【https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac014/6547681】. An adversarial confounder encoder, trained to predict known confounders from the node embeddings while the main eqGNN is optimized to minimize this prediction, forces the embeddings to be invariant to those factors【https://bioscipublisher.com/index.php/cmb/article/html/3925/】. Invariance has been shown to uncover subgraph patterns that are statistically indistinguishable under different confounder realizations—these are the nonconfoundable motifs described in theoretical work on network reconstruction with missing data【https://pmc.ncbi.nlm.nih.gov/articles/PMC3822397/】. By constraining the eqGNN to retain only information that cannot be linearly predicted from confounder proxies, the model is expected to highlight topological motifs (e.g., feed‑forward loops, cliques, or bridge nodes) that are mechanistically stable across contexts and thus more likely to represent bona fide aging‑related drug targets.

Testable Predictions

1. eqGNNs equipped with an adversarial confounder module will achieve higher out‑of‑sample AUROC on held‑out aging‑target datasets (e.g., DrugAge, Geroprotectors) when evaluated across multiple batches or tissue‑specific protein‑protein interaction (PPI) networks, compared with baseline eqGNNs that rely only on pre‑training【https://academic.oup.com/bib/article/26/5/bbaf554/8303310】.
2. The top‑ranked nodes identified by the confounder‑invariant eqGNN will overlap significantly with experimentally validated longevity genes (e.g., from the GenAge database) and will be enriched for known nonconfoundable motifs such as tri‑adic cliques and bow‑tie structures, whereas baseline eqGNN rankings will show no such enrichment.
3. Perturbation (CRISPR knock‑down or small‑molecule inhibition) of the top‑ranked invariant nodes in human fibroblasts will produce a consistent shift in senescence‑associated secretory phenotype (SASP) markers across at least three different donor lines and two culture conditions, while perturbations of baseline‑ranked nodes will yield heterogeneous or null effects.

Experimental Design

• Data: Collect aging‑relevant PPI networks from GTEx (multiple tissues), BioGRID, and STRING; label edges with known aging‑target interactions from DrugAge and Geroprotectors.
• Models: (a) Baseline eqGNN with 3D structure (e.g., SE(3)-Transformer) and self‑supervised pre‑training; (b) Same architecture augmented with an adversarial confounder head predicting batch, tissue, and donor ancestry via gradient reversal.
• Training: Optimize primary link‑prediction loss while maximizing confounder prediction loss (gradient reversal λ tuned via validation).
• Evaluation: Perform five‑fold cross‑validation where each fold holds out an entire tissue or batch; compute AUROC, AUPRC, and calibration. Additionally, conduct an external test on a completely independent aging‑interaction set (e.g., from the Human Ageing Genomic Resources).
• Validation: Use network motif detection (Mfinder) to extract frequency of tri‑adic cliques, feed‑forward loops, and bridge nodes among top‑5 % ranked proteins; compare enrichment via hypergeometric test against degree‑matched random sets.
• Perturbation: Select top‑10 invariant and baseline nodes; employ siRNA knockdown in primary human fibroblasts from three donors; measure SA‑β‑gal, p16^INK4a, and IL‑6 secretion after 72 h under normoxic and low‑serum conditions.

Potential Pitfalls and Alternatives

If adversarial training fails to remove confounder information (e.g., confounder head still predicts >80 % accuracy), we will increase the capacity of the confounder encoder or switch to a mutual‑information‑based invariance loss (e.g., DOMINO)【https://pmc.ncbi.nlm.nih.gov/articles/PMC11494969/】. Should the invariant eqGNN not outperform the baseline, we will examine whether hidden confounders are non‑linear or higher‑order, prompting the use of causal graph discovery algorithms (e.g., LiNGAM) to generate explicit confounder graphs for integration as auxiliary node features.

By directly modeling and removing confounder influence during representation learning, this approach aims to extract the nonconfoundable core of the aging interactome—a set of topologically robust, mechanistically interpretable nodes that can be prioritized for preclinical validation with greater confidence than current correlation‑driven GNN predictions.

Hypothesis

Integrating Disentangled Interpretable Multimodal Attention Fusion (DIMAF) with Deep Conditional Transformation Models (DCTMs) and median‑SHAP will reveal non‑proportional, time‑varying hazard pathways that link specific multi‑omic signatures (e.g., mitochondrial DNA methylation, circulating proteomic inflammation clusters) to exceptional longevity, outperforming standard Cox, joint models, and gradient‑boosting baselines on centenarian cohorts.

Mechanistic Rationale

DIMAF learns modality‑specific and shared latent representations via intra‑ and inter‑modal attention, isolating omics dimensions that drive survival signal while suppressing noise [2]. DCTMs extend the Cox framework by modeling the conditional distribution of survival times through flexible Bernstein polynomials, capturing non‑linear and non‑proportional hazard shapes that arise when biomarker trajectories interact with age‑dependent processes [1]. When DIMAF‑derived latent factors are fed as time‑varying covariates into a DCTM, the model can estimate how each disentangled omics module modulates the baseline hazard across the lifespan, a capability absent in current siloed approaches. Median‑SHAP anchors explanations to the median predicted survival, mitigating bias from right‑skewed censoring distributions common in longevity data [3][4].

A further mechanistic layer posits that mitochondrial epigenetic drift alters the dynamic coupling between oxidative‑stress proteomics and inflammasome activation, producing a time‑dependent hazard inflection point around the ninth decade. This inflection would appear as a change‑point in the Bernstein polynomial coefficients estimated by the DCTM, detectable only when the mitochondrial methylation latent DIMAF axis is included.

Testable Predictions

1. Predictive Performance: A DIMAF‑DCTM median‑SHAP pipeline will achieve a higher concordance index (C‑index ≥0.88) and integrated AUC (≥0.86) on a held‑out longevity cohort (e.g., New England Centenarian Study with multi‑omics and quarterly clinical measures) compared to:
   • Standard Cox with baseline omics
   • Extended Cox with time‑varying covariates
   • Joint models of longitudinal biomarkers and survival
   • Gradient‑boosting survival models (XGBoost, GBM) and Random Survival Forests
2. Non‑Proportional Hazard Detection: The estimated Bernstein polynomial coefficients will show a statistically significant change in slope at ~85‑90 years only when the mitochondrial methylation DIMAF latent variable is included (p < 0.01, likelihood‑ratio test).
3. Median‑SHAP Interpretation: Median‑SHAP values will identify the mitochondrial methylation axis and a specific inflammasome proteomic cluster as the top two shared latent features contributing to hazard reduction in long‑lived survivors, with SHAP values consistently directional across bootstrap resamples.
4. Simulation Validation: Survival times generated via the Lambert W function with continuous time‑varying covariates mimicking the identified omics‑hazard dynamics will be recovered by the DIMAF‑DCTM model with parameter bias <5%, confirming model identifiability.

Experimental Design

• Data: Multi‑omics (whole‑genome methylation, plasma proteomics, transcriptomics) and longitudinal clinical covariates (IL‑6, CRP, gait speed) from ≥1,200 individuals aged ≥80, with ≥5‑year follow‑up and known vital status.
• Preprocessing: Normalize each omics layer, apply DIMAF to obtain shared and modality‑specific latent factors (dimensionality = 20 per modality).
• Modeling: Fit a DCTM where the conditional transformation function depends on the DIMAF latent factors as time‑varying covariates; estimate via maximum likelihood with Bernstein polynomial order selected by cross‑validation.
• Interpretation: Compute median‑SHAP for each latent factor at decadal intervals; assess stability via 1,000 bootstrap samples.
• Comparison: Train baseline models on identical data; evaluate using time‑dependent C‑index, integrated Brier score, and calibration plots.
• Falsifiability: If the DIMAF‑DCTM does not surpass the best baseline by ≥0.03 in C‑index, or if no change‑point in hazard coefficients is detected around the ninth decade, the hypothesis is refuted.

Expected Impact

Successful validation would provide a mechanistically interpretable framework that bridges high‑dimensional omics with flexible hazard modeling, uncovering timing‑specific biological drivers of exceptional longevity and offering a template for other complex aging phenotypes.

Hypothesis

Adding a fourth epigenomic layer—specifically, DNA methylation quantitative trait loci (meQTLs)—to existing multi‑omics pipelines converts correlative associations into directional causal inferences, thereby improving disease subtyping, biomarker discovery, and prediction of therapeutic response beyond what genomics‑proteomics‑metabolomics alone can achieve.

Rationale

Current multi‑omics approaches excel at capturing cross‑layer correlations but struggle to infer causality [7]. Epigenetic modifications sit mechanistically between genetic variation and downstream protein/metabolite abundances; meQTLs link genotypes to methylation states, which in turn influence gene expression and protein levels. By explicitly modeling these genotype‑>methylation>proteom/metabolite chains, we can anchor observed multi‑omics changes to upstream genetic drivers, satisfying Mendelian randomization criteria for causality.

MeQTL data are increasingly accessible through large consortia (e.g., GOGT, BIOS QTLs) and can be integrated with existing proteogenomic frameworks that already combine proteomics and Mendelian randomization [6]. Deep‑learning multi‑view autoencoders can accommodate the additional heterogeneity [3], while similarity network fusion preserves sample relationships even with missing methylation values [4].

Testable Predictions

1. Improved risk prediction – In an independent cohort of Alzheimer’s disease patients, a model trained on genomics + proteomics + metabolomics + meQTLs will achieve an AUROC ≥ 0.78, a statistically significant increase over the 0.703 AUROC reported for transcriptomics‑plus‑covariates random forest [1].
2. Causal biomarker identification – Proteins whose abundance is significantly mediated by methylation (i.e., significant genotype→methylation→protein path) will show enrichment for known drug targets and will be validated by CRISPR‑dCas9 methylation editing in vitro, resulting in measurable changes in protein level and downstream metabolite flux.
3. Treatment response stratification – In glioma, patients stratified by multi‑omics risk scores that include meQTL‑derived causal proteins will exhibit a ≥ 20 % difference in progression‑free survival compared to stratification using genomics‑proteomics‑metabolomics alone [2].

Experimental Design

• Data collection – Obtain matched germline genotype, whole‑blood or tissue DNA methylation, bulk proteomics (LC‑MS/MS), and untargeted metabolomics from 500 Alzheimer’s cases and 500 controls (or 300 glioma patients with treatment outcomes).
• Processing – Call meQTLs using MatrixEQTL, normalize each layer with combat‑seq, and impute missing methylation values via k‑nearest neighbours.
• Modeling – Train a multi‑view autoencoder to learn joint latent representations; attach a downstream gradient‑boosted classifier for outcome prediction. Compare AUROC against baseline models lacking the methylation view using DeLong’s test.
• Mediation analysis – Apply causal mediation analysis (R package ‘mediation’) to test genotype→methylation→protein paths; retain proteins with significant indirect effects (p < 0.01 after FDR correction).
• Validation – Select top three causal proteins; use CRISPR‑dCas9‑TET1 or dCas9‑DNMT3A to demethylate/methylate promoter CpGs in iPSC‑derived neurons or glioma lines; measure protein abundance (Western blot) and metabolite shifts (targeted LC‑MS).

Potential Pitfalls & Mitigations

• Confounding by cell‑type composition – Adjust methylation data with reference‑based deconvolution (e.g., MeDeCom) before meQTL mapping.
• Horizontal pleiotropy – Use MR‑Egger and weighted median methods to assess pleiotropic bias in mediation estimates.
• Model overfitting – Employ nested cross‑validation and hold‑out test set; limit latent dimensions via explained variance threshold.

If the predictions hold, this work demonstrates that epigenomic layering converts multi‑omics from a pattern‑finding exercise into a causal discovery engine, directly addressing the field’s foremost bottleneck [7] and moving biomarker identification toward actionable therapeutic targets.

Hypothesis

Integrating spatial multi‑omics with circulating tumor DNA (ctDNA) kinetics using a graph neural network (GNN) that models metabolite exchange can forecast the emergence of therapy‑resistant subclones before clinical relapse.

Rationale

It's now possible to profile tumors at single‑cell resolution while simultaneously tracking ctDNA in plasma, giving high AUCs for early detection [1]. Spatial technologies map genomics, transcriptomics, proteomics and metabolomics within tissue architecture [2]. ctDNA dynamics reflect tumor burden and clonal shifts [3]. However, current models treat each modality separately, missing the spatial‑metabolic feedback loops that drive clonal selection. We propose that a GNN that treats each spatially resolved voxel as a node, with edges weighted by inferred metabolite fluxes (e.g., lactate, glutamine) and linked to ctDNA variant frequencies, will capture the selective pressures that generate resistant phenotypes.

Testable Predictions

1. In a longitudinal cohort of patients receiving targeted therapy, the GNN‑derived risk score will rise significantly (≥2‑fold) at least 8 weeks before radiographic progression.
2. Patients whose spatial‑metabolic GNN score remains low despite rising ctDNA will show pseudoprogression rather than true resistance.
3. Experimental blockade of a top‑predicted metabolite pathway (e.g., inhibiting MCT1 lactate transport) in patient‑derived organoids will reduce the GNN score and delay outgrowth of resistant clones.

Experimental Design

• Collect pretreatment, on‑treatment (every 2 weeks), and post‑progression tumor biopsies for spatial multi‑omics (10x Visium or Slide‑seqV2) and matched plasma for ctDNA sequencing.
• Build a heterogeneous graph: nodes = spatial omics features (gene expression, protein abundance, metabolite levels); edges = diffusion‑based metabolite exchange estimated from extracellular fluid measurements.
• Train a GNN to predict ctDNA variant allele frequency changes; validate on held‑out samples.
• Prospective testing: compute risk scores in real time and compare with clinical outcomes.
• Organoid assays: treat with inhibitor of predicted metabolite transporter; measure clonal composition via single‑cell DNA seq over 14 days.

Potential Implications

If validated, this framework would enable pre‑emptive therapy adjustment, reduce overtreatment, and inform trial designs that target microenvironmental metabolites alongside cancer‑cell intrinsics.

References

[1] Multi‑omics integration improves early‑detection AUCs (https://www.pnas.org/doi/10.1073/pnas.1521919112) [2] Spatial multi‑omics platforms (https://pmc.ncbi.nlm.nih.gov/articles/PMC12766144/) [3] ctDNA dynamics in monitoring tumor burden (https://pmc.ncbi.nlm.nih.gov/articles/PMC12226543/)

Hypothesis

We propose that integrating mitochondrial redox-sensitive signaling pathways—specifically the NAD+/NADH ratio and ROS-mediated activation of Nrf2/KEAP1—into aging digital twins creates a closed-loop system where real-time biomarker fluxes directly modulate intervention dosing, transforming static forecasts into adaptive therapeutic agents.

Mechanistic Rationale

Current digital twins rely on intermittent biomarker snapshots (epigenetic clocks, CRP, wearables) to predict trajectories but lack fast‑acting physiological readouts that reflect instantaneous cellular stress. Mitochondrial redox state changes within minutes to hours in response to nutrients, exercise, or drugs, and can be sampled non‑invasively via circulating cell‑free DNA oxidation signatures or wearable‑detectable NADH fluorescence. By feeding these dynamic redox indices into the AI core, the twin can instantly simulate the impact of a candidate intervention on downstream pathways (e.g., mTOR inhibition, senescent cell clearance) and adjust dosing before maladaptive cascades accumulate.

Testable Predictions

1. In a cohort of older adults undergoing rapamycin supplementation, real‑time NAD+/NADH ratios measured via a wearable biosensor will predict short‑term changes in epigenetic age acceleration (ΔDNAmAge) more accurately than baseline CRP or IL‑6 levels.
2. Digital twin simulations that incorporate redox feedback will reduce the variance of predicted frailty index trajectories by at least 30% compared with models using only static multimodal inputs.
3. Closed‑loop adjustment of rapamycin dose guided by redox‑triggered alerts will achieve equivalent or superior improvement in gait speed with lower cumulative drug exposure than fixed‑dose regimens in a randomized crossover trial.

Experimental Design

• Enroll 120 participants aged 70‑85, stratify by sex and baseline frailty.
• Equip each with a multimodal wearable that records heart rate variability, activity, and a prototype NAD+/NADH fluorescence sensor (validated against plasma metabolomics).
• Collect baseline multimodal data (epigenetic clock, proteomics, EHR) to initialize each participant's digital twin per 1 and 2.
• Randomize to either (a) standard care with quarterly clinician‑reviewed twin predictions, or (b) closed‑loop arm where the twin continuously ingests redox data and recommends rapamycin dose adjustments via a clinician-in-the-loop interface.
• Primary outcome: change in frailty index over 6 months; secondary: epigenetic age acceleration, incidence of adverse events, total rapamycin exposure.

Potential Challenges and Mitigations

• Sensor accuracy: address by calibrating wearable redox readouts against monthly blood draws and using error‑correction layers in the AI model 4.
• Algorithmic opacity: employ explainable‑AI modules to surface which redox features drove dose changes, satisfying transparency requirements 5.
• Generalizability: include diverse socioeconomic strata to counter performance biases noted in prior work 5.

If validated, this hypothesis would transform digital twins from prognostic dashboards into physiologically responsive actuators, fulfilling the promise of precision gerontology.

Theme: Preventive Health + Digital Care

Technical thesis: Agentic preventive-care systems that combine wearable signals, medication adherence tracking, and personalized intervention prompts can identify early deterioration and reduce avoidable chronic-care escalations.

Investor angle: Solutions proving reduced acute events and better adherence can unlock strong payer/provider demand through outcomes-based contracts.

Leading indicators:

• Early-risk detection lead time before acute episodes
• Medication adherence improvement by cohort
• Avoidable ER visit rate for managed chronic populations
• Monthly cost-of-care trend for intervention vs control groups

90-day falsifiable predictions:

1. High-risk alerts identify deterioration earlier by clinically meaningful windows.
2. Adherence improves by >=12% in intervention cohorts.
3. Avoidable acute-care utilization decreases by >=10%.

Invalidation condition: If intervention cohorts show no measurable improvement in adherence or acute-care reduction, model personalization and workflow integration are insufficient.

Hypothesis

Incorporating tissue‑specific metabolic state variables (AMPK activity, mTORC1 signaling, cytosolic acetyl‑CoA concentration) as dynamic node features in graph neural networks will significantly improve the prediction of autophagy‑modulating drug efficacy across aged tissues, whereas models that ignore these variables will systematically overestimate benefit in metabolically stressed organs such as liver and underestimate it in reserve tissues like skeletal muscle.

Mechanistic Basis

Autophagy functions as a rationing system that is toggled by the cell’s perception of siege conditions. Nutrient‑sensing kinases (AMPK, mTOR) and metabolite sensors (acetyl‑CoA) directly gate the transcriptional and post‑translational control of core autophagy genes (e.g., BECN1, LC3) [4, 5]. In aged cells, chronic elevation of acetyl‑CoA suppresses Atg gene expression, while AMPK activation can override this block [5]. Consequently, the same drug that stimulates autophagy initiation may be futile in a tissue with high acetyl‑CoA/low AMPK (e.g., aged liver) but protective where AMPK dominates (e.g., aged muscle). Current GNN architectures for drug‑target interaction predict binding affinity in a static topological context and lack any representation of these metabolic switches [1, 2].

Prediction

A GNN that augments each protein or drug node with three continuous features—phospho‑AMPK/total AMPK ratio, phospho‑S6K/total S6K ratio (mTORC1 read‑out), and measured cytosolic acetyl‑CoA—will achieve a statistically significant increase in AUROC (≥0.07 improvement) when classifying compounds as autophagy‑effective versus ineffective in aged mouse tissues, compared to a baseline GNN that uses only sequence‑ and network‑derived features.

Experimental Design

1. Data curation – Compile a dataset of FDA‑approved and experimental compounds with reported effects on autophagy flux (LC3‑II turnover, p62 degradation) in primary cells from aged (≥24 mo) mouse liver, skeletal muscle, heart, and brain. Include quantitative tissue‑specific measurements of phospho‑AMPK, phospho‑S6K, and acetyl‑CoA from the same samples [3].
2. Model construction – Build two graph architectures:
   • Baseline: heterogeneous DTI-HETA‑style graph with GCN embeddings and GAT attention, using only protein sequence similarity, chemical fingerprints, and known interaction edges.
   • Metabolic‑augmented: identical topology but each protein node appended with the three metabolic features; drug nodes retain chemical descriptors.
3. Training & validation – Use five‑fold cross‑validation stratified by tissue to predict binary efficacy (effective if flux increase >30 % vs control). Optimize hyper‑parameters independently for each architecture.
4. Statistical test – Compare AUROCs via DeLong’s test; a p‑value <0.01 indicates significant improvement.

Potential Outcomes

• If the metabolic‑augmented GNN outperforms the baseline, the hypothesis is supported: encoding siege‑state metabolism enables models to discern when autophagy modulation will be beneficial versus futile, bridging the current methodological chasm between DTI prediction and aging‑context autophagy biology.
• If performance does not improve, the hypothesis is falsified, suggesting either that the selected metabolic readouts do not capture the relevant regulatory node or that additional layers (e.g., lysosomal capacity, redox state) are required for accurate prediction.

This framework directly links the siege‑state conception of autophagy to a concrete, testable modification of graph‑based drug‑target modeling, providing a path toward context‑aware therapeutic discovery in aging.