Machine learning for diagnostics design
Our molecular diagnostic target identification and synthetic biology design processes are powered by innovative bioinformatics and machine learning approaches.
The combination of synthetic biology and advanced bioinformatics approaches drives our continued innovation, and is applicable to all molecular diagnostic technologies.
Non-biased high throughput target identification
Using bioinformatics workflows, we can identify molecular diagnostic targets optimised for desired characteristics. This allows us to find genetic targets with specificity to multiple strains or species or to a single pathogen as required.
Synthetic biology designed through machine learning
Using machine learning we have developed an automated method for designing synthetic nucleic acid polymers which work in combination to detect target genetic sequences with desired specificity and high sensitivity.
Our process from target identification to molecular diagnostic design is highly automated enabling rapid and high throughput diagnostic test design at scale. This enables both a quick turnaround for partnering companies and a rapid pace of diagnostic innovation.