How 3billion’s Diagnostic Models Are Transforming Rare Disease Testing

Genetic test | 25. 06. 26

🧬 Part of the Decoded Series: Behind Our Diagnostic Technologies

Welcome to Decoded, a series where we break down 3billion’s patented technologies into clear, understandable stories. Each post in this series explores the innovations behind our diagnostics—how they work, why they matter, and how they ultimately improve patient outcomes.

In this edition, we revisit a fundamental challenge in rare disease diagnostics: aligning clinical reasoning based on symptoms with genomic data centered around variants.

Physicians start from symptoms.
Genomic data starts from genes.

What if we could combine both into a single mathematical model?

Why was this technology needed?

In rare disease diagnostics, it’s often not enough to find a variant. Clinical decision-making demands a holistic interpretation—taking into account not just the patient’s genome, but also their symptoms. The problem? These data types don’t always speak the same language.

3billion developed a patented approach to bridge this gap. The innovation corrects diagnostic probability by jointly analyzing the symptom-disease correlations and gene-disease correlations—providing a more refined and accurate diagnostic likelihood.

How does it work?

This patented system includes three steps:

1. Symptom-to-disease correlation analysis
→ Identifies potential diseases based on reported symptoms.

2. Gene-to-disease correlation analysis
→ Assesses variants in the patient’s genome and their associations with diseases.

3. Combined probability calculation
→ Uses both analyses to compute a corrected probability for disease expression.

This three-layered logic helps reduce false positives and supports clinical teams in prioritizing the most plausible diagnoses.

FAQ

Q: Why is it necessary to combine symptom and genetic data for diagnosis?
A: Because symptoms can be nonspecific or overlapping, relying on genetics alone may miss relevant conditions. This approach improves diagnostic yield by integrating two types of evidence.

Q: Does this system work only for known diseases?
A: It’s most effective for diseases with established symptom and variant associations, but it can support novel variant interpretation when symptoms provide useful clinical clues.

Q: Is this used in every 3billion test?
A: This system powers part of our interpretation logic and underlies the decision-making processes in our GEBRA platform.