The methodology behind S.O.U.V.E.R.A.I.N. — causal inference in multi-omics systems, local-first biological intelligence, and the architecture of sovereign health infrastructure.
A formal treatment of the causal inference architecture underlying S.O.U.V.E.R.A.I.N. — covering the Physiological Bus data model, PC-algorithm-based causal discovery, and interventional prediction accuracy across seven omic layers.
We present a novel architecture for real-time causal discovery in high-dimensional biological time-series data. Our Physiological Bus model fuses seven omic layers — genomic, proteomic, metabolomic, epigenomic, microbiomic, lipidomic, and transcriptomic — into a unified causal graph updated at sub-second intervals on consumer hardware.
Unlike correlation-based health platforms, our system applies the PC algorithm with Fisher Z-tests for conditional independence, producing interventional predictions rather than observational associations. Evaluated across 1.2 million data points from 847 participants, the system achieves 89% accuracy on 30-day intervention outcomes.
We partner with longevity research institutions, academic labs, and clinical programmes. Academic pricing available.
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