Genetic codes are an information system where causation matters more than correlation. Statistical genomics has produced extraordinary pattern-finding capabilities. It has not produced causal understanding of biological mechanism. The SCI framework was described, from its first formulation, as applicable to any unknown information universe โ including the genome.
Genome-wide association studies identify statistical correlations between genetic variants and observable traits at extraordinary scale and precision. What they do not identify is the causal mechanism connecting the variant to the trait โ the biological pathway, the regulatory interaction, the protein expression consequence that actually produces the phenotype. This gap between statistical association and causal mechanism is not a data problem. It is a fundamental limitation of the question being asked. More data does not answer a different question.
The participation matrix framework, in its most general formulation, encodes the causal participation of information components in observed outcomes. In the genome, information components are regulatory elements, coding sequences, expression states. Observed outcomes are phenotypic states, protein expression levels, disease manifestations. PM^kl applied to biological information encodes not statistical co-occurrence but causal participation โ which genetic events are causally implicated in which biological states, and to what strength.
Genomics is the most forward-looking application in the SCI portfolio. The theoretical applicability is established in the foundational patents. Product development requires significant domain-specific work beyond what SCI is currently resourced to pursue. Licensing of the State Navigation framework for genomics and biological information applications is available through ATTVC for research institutions and biotech companies with the domain expertise to build on the framework.
US 20222245109 A1 (State Navigation) explicitly covers application to any information domain where causal association and participation matrices can be defined. The biological information universe is a named example in the foundational theory. Priority date July 21, 2019. Licensing available through ATTVC.