Our Research
Peer-reviewed publications, patent portfolio, and technical documentation underlying the DNAI physics-constrained cancer digital twin platform.
Research Publications
Original research from the DNAI project. All preprints are freely available on bioRxiv, medRxiv, or arXiv. Journal submissions in progress.
Domain Shift is a Feature, Not a Bug: Distributionally Robust Optimization Outperforms Harmonization in Clinical Foundation Models
Feb 2026
We challenge the harmonization paradigm, demonstrating that site-specific variance encodes critical prognostic information. Group DRO with Pooled Cox achieves C=0.718 on CPTAC, outperforming all baselines including ComBat and CORAL.
The Plural Twin: Quantifying Treatment Policy Stability via Set-Valued Cancer Digital Twins
Feb 2026
We introduce Plural Twins, a set-valued framework where each patient is represented as a distribution of outcomes. 82.9% of patients show policy instability; for 1 in 6, the optimal treatment depends on the algorithm's risk tolerance.
Runtime Reliability Labeling for Safe Deployment of Oncology AI Under Distribution Shift
Feb 2026
A unified framework for certifying when a clinical AI prediction is reliable enough for decision-making. Per-patient transportability certificates, structured abstention, and evidence-completion recommendations.
The Sim-to-Real Scaling Paradox: Biological Heterogeneity Reverses Transfer Learning Gains in Cancer Digital Twins
Feb 2026
We report a counterintuitive data scaling paradox: expanding PDX training data from 128 to 573 samples degrades clinical prediction. Multi-cancer alignment erases biology-preserving variance through shortcut domain adaptation.
The Glass Cannon Phenotype: High Predicted Benefit but Low Robustness to Biological and Dosage Perturbations
Feb 2026
We identify a clinically actionable phenotype defined by the intersection of high predicted treatment benefit and low robustness to perturbation. These patients (7.0%) show median OS of 478d with 71.1% event rate — the worst outcomes despite high expected benefit.
Practical Identifiability Failure in Physics-Constrained Cancer Models: The Therapeutic Controllability Index
Feb 2026
We report a fundamental identifiability failure: drug sensitivity (beta) occupies 0.14% of its allowed range under survival-only supervision. Rather than treating this as a defect, we introduce the Therapeutic Controllability Index to quantify treatment authority per patient.
Patent Portfolio
8 U.S. Provisional Applications filed. Covering physics-constrained simulation, domain adaptation, uncertainty quantification, runtime safety, and risk-averse optimization.
#1: Sim-to-Real Transfer
Physics-Constrained Sim-to-Real Transfer Learning
#2: Collapse Prevention
Preventing Metabolic Scaling-Induced Collapse
#3: PESD & Missing Modalities
Uncertainty-Calibrated Missing Modality Imputation
#4: Pathway-Aware GRL
Ontology-Guided Autogradient Modulation
#5: ExecutionToken & Solver Interlock
Adjoint Sensitivity & Physics-Constrained Gradient Topologies
#6: Group DRO for Digital Twins
Distributionally Robust Training (DRO)
#7: Risk-Averse Stochastic Optimization
Stabilized Stochastic Inference and Risk-Averse Optimization in Physics-Constrained Oncology Digital Twins
#8: Runtime Gating & Solver Interlock
Cryptographically Enforced Runtime Resource Gating in Differential Equation Solvers via Memory Allocation Interlock
Whitepapers
Deep-dive technical documentation on the architecture, algorithms, and validated performance of the platform.
Foundation White Paper
Neuro-Symbolic architecture overview. H-BDVAE, dual-path system, and physics-constrained simulation.
The Dark Matter of Oncology
Why genomics alone cannot predict resistance. Non-Mutational Resistance and the failure of standard AI.
Sim-to-Real Architecture
Split-Source Transfer Learning for predictive oncology. From organoids to patients via domain separation.
Presentations & Media
Visual walkthroughs of the platform architecture and patent portfolio.
Physics-Constrained Cancer Dynamics
15-slide visual walkthrough of the sim-to-real architecture, separated-state ODEs, and patent portfolio.
Using Physics to Translate Mouse Cancer
AI-generated podcast covering patent innovations, cross-species transfer learning, and clinical implications.
Validated Performance
Key metrics across internal and external cohorts
Research Use Only
All publications and methods described here are for research purposes only and have not been cleared or approved by any regulatory authority for clinical use. The DNAI platform is not a medical device. Patent applications are U.S. Provisional Applications; no patents have been granted.
Interested in collaborating?
We welcome academic collaborations, validation partnerships, and licensing discussions.