Annotating Genome Variant Data for a Quantum Bayesian Network

Making the data speak a language we can work with

Somewhere in the human genome lies the answer to a life-changing question: Why is this patient ill?

But the genome does not speak clearly. It whispers in codes. There are millions of variants, most of which are benign, a few cryptic and some with devastating effects. When diagnosing rare diseases, these subtle signals often get lost in the noise, and traditional methods struggle to connect the dots.

This is where probabilistic reasoning comes into play. We're building a Quantum Bayesian Network (QBN): a model that not only connects mutations to disease, but infers, weighs, and adapts. It brings uncertainty to the public and enables the kind of nuanced medical insights that rare disease diagnosis requires.

But before this machine learning algorithm can work, it needs something crucial: data. In this post, we described the types of data needed to build a Quantum Bayesian Network for rare disease diagnosis.

And we started our data collection journey by downloading and processing genetic data to ground our model in biology. What we got was a flat, clinical .vcf file. It's full of genomic variants, but without meaning.

It's time to turn this raw data into annotated, interpretable genetic signals and lay the foundation for a system that can diagnose rare diseases.