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Highly efficient

We use our proprietary and patented mathematical framework and high-performance GPU computing to run complex disease studies in just a few days. Even with extensive validation, interpretation, and evaluation of the results, our studies deliver ground-breaking discoveries at exceptional speed.

Speed and scale

For complex diseases, existing tools like Genome Wide Association Studies (GWAS) and Machine Learning (ML) do a poor job of finding clinically relevant signal. A common response is to assemble larger and larger populations to help identify ultra-rare variants. However, this approach is slow and expensive, often only marginally effective, and can be counterproductive from a clinical relevance perspective.

By contrast, our AI and combinatorial analytics generate much deeper disease insights much faster from much smaller datasets.

We routinely work with just several hundred patient cases compared against controls, or amongst themselves, to explain a quantitative trait, for example, different levels of drug response in a clinical trial.

Speed and scale-min

More signal from patient data

A good example is the worldwide search for explanations of the difference in COVID-19 symptoms and severity across patients. Groups such as 23andMe and COVID-19 HGI took 9-18 months to assemble data on up to 2 million patients and still reported limited findings (1-6 genes).

Our first COVID analysis was completed 12 months earlier than COVID-19 HGI – within 3 weeks of the first UK Biobank dataset on just 750 patients becoming available. We were the first to report 68 genes associated with severe disease and linked to all the major symptoms. These findings (including 48 of the genes and 13 drug repurposing candidates) have since been extensively validated by our collaborators and independently by many other groups around the world.

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