From Static Liability to Dynamic Intelligence
Ten Foundational Capabilities that represent a paradigm shift in genomic analysis, moving beyond simple interpretation to enable prediction, proactive de-risking, automated compliance, and dynamic, longitudinal intelligence.
Static Reports are a Systemic Failure of Audit, Evidence, and Time
Audit Collapse
Most legacy processes lack any cryptographically verifiable audit trail. Analytic and reporting steps cannot be retraced, and there is no versioned record for evidence review or legal defense.
This creates profound regulatory and operational risk, rendering data inadmissible under ARCS, HIPAA, or GDPR frameworks.
Static Interpretation Failure
Legacy reports are "artifacts frozen in time," incapable of incorporating new evidence from databases (ClinVar, gnomAD) or evolving guidelines (ACMG, CPIC).
Real-world re-classification rate for genetic variants within two years
Uncertainty Mismanagement
The inability to trace provenance or update data leads to a permanent state of ambiguity. Risks, evidence gaps, and ancestral bias are concealed rather than quantified and managed.
The Helios Supra-Framework Transforms Static Reports into Living, Auditable Intelligence
An integrated, compliance-ready architecture that replaces static reporting with a dynamic, scenario-resilient system built on four core, interoperable components.
V-Framework Scenario Fusion
Enables scenario-layered modeling (Baseline, Escalation, De-escalation) to stress-test outcomes.
ARCS/ARCF Overlays
Automates multi-jurisdictional regulatory compliance (FDA, EMA, PMDA, etc.) at the clause level.
ECIA-7 Compliance
Enforces cross-border privacy and mandates equity (EO diff=0) across all analyses.
IQAS v5.x Audit Gates
Injects cryptographically verifiable, immutable audit checkpoints (SHA256, UTC) into every process step.
Ten Foundational Capabilities, Converting Data into Predictive Power
Prediction & Simulation
Predict Complex Biological Outcomes and Simulate Future Scenarios
The framework fuses AI-driven predictive modeling with deterministic compliance to forecast everything from HDR efficiency to genotype-phenotype effects.
V-Framework Scenarios for HDR Efficiency Predictor
Baseline
The predicted outcome under canonical, optimized experimental conditions
e + <10nt Edit + HDR Enhancer V2
Escalation
Models adverse outcomes, such as NHEJ dominance or repair failure
RNP Only - No Template
De-escalation
Models optimized outcomes after protocol adjustment
bp Max (PAM Proximal + Template)