Prediction under declared uncertainty.
CytoMind simulates against the twin — and is explicit about when it will not. A model that answers every question regardless of what it has been given is not confident; it is unsafe.
Builds the model input from twin layers as at a specified recorded time, and declares completeness and staleness per layer.
Refuses, with a stated reason, where required layers fall below completeness or recency thresholds.
Forward predictions for defined endpoints over defined horizons.
Re-runs the trajectory under a specified pharmacological, nutritional or behavioural change.
Calibrated intervals, separating missing-data uncertainty from model-inherent uncertainty.
Per-prediction feature attribution at a granularity a clinician or scientist can evaluate.
Compares production inputs against the validation population and raises signal on divergence.
Simulation is not recommendation.
A simulation says: under this model, this trajectory follows. A recommendation says: do this. The first is a computation; the second is a clinical act.
CytoMind emits trajectories — intervention parameters, predicted change, interval and attribution — and no imperative. Translating a trajectory into a proposed action happens only in TheraMind, where review and sign-off exist. That separation is what allows simulation to serve research and population work without inheriting the clinical surface’s regulatory obligations.
A dietary change and a drug change, in the same units.
Presenting a simulated nutritional change and a simulated therapy change with the same endpoint, the same interval treatment and on the same screen is a small design decision with a large clinical consequence: it makes the non-pharmacological option visible at the moment of decision, when it is otherwise routinely displaced by the option that has a prescription attached.
Run simulations against your data.
Research groups, drug discovery teams and clinical programmes.