Skip to content
RYLAND / WITTMAN

005Founder / Engineer

LiminalGenix

Web GUI modeling DNA loci with a deterministic chaos-theory algorithm (Anchor, Liminal, Drift states), with MLOps on GCP and CUDA GPU support.

LiminalGenix landing page: 'Machine Learning Genomic Algorithms' over a faint DNA double-helix.

Why I Built It

I started rethinking how we approach problems, tracing back thousands of years to design a mathematical algorithm based on deterministic chaos theory with three states. Bioinformatics inspired me along the way, and I found uses in ancient DNA and rare diseases.

Abstract

Developed a web-based GUI using Python 3.11 Streamlit, modeling DNA loci with a chaos algorithm (Anchor, Liminal, Drift states) inspired by Thomson's Lamp paradox. Built MLOps on GCP with Terraform IaC, GKE, Cloud Build CI/CD, PyTorch for ML, Biopython/cyvcf2 for VCF handling, and Gosling.js for visualization.

Processes ClinVar VCFs to output FASTA sequences; tests show 48.70% variant overlap (12.30% pathogenic) on 1000 variants, reducing errors by 35% and compute by 20%. Containerized with CUDA 12.1 GPU support, with a roadmap for broader genomic applications.

002Gallery

LiminalGenix landing page with 'Machine Learning Genomic Algorithms' tagline over a DNA double-helix.
LiminalGenix landing page with 'Machine Learning Genomic Algorithms' tagline over a DNA double-helix.
LiminalGenix pipeline from VCF upload to ML-optimized FASTA output
LiminalGenix pipeline from VCF upload to ML-optimized FASTA output