Resonant genomic and epigenetic mapping for precision prevention

This project focuses on identifying hereditary disease risk factors through resonant genomics and self-tuning epigenetic analysis. Rather than relying on traditional CRISPR–Cas9 editing, the approach centers on a non-invasive, field-based gene information analysis, examining the resonance between inherited and acquired genetic patterns.

The system shifts the focus from static genetic sequencing to the dynamic behavior of gene expression — exploring how genes respond to environmental, biochemical, and emotional influences. Through this lens, not only can risk-associated genes be identified, but also the resonant modulation points where natural self-regulation and cellular repair processes can be reactivated.

This research operates at the intersection of resonant bioinformatics and quantum genomics, aiming to build a predictive framework capable of detecting hereditary risks early while supporting the restoration of individual genetic balance.

The project forms part of the Resonant-AI Genomic Program, which views genetic information not merely as code, but as a living, holographic data field.