Our Solution

We are leveraging the power of machine learning to rigorously assess the efficacy of cancer prevention therapeutics. 

From tissue samples, we isolated an extensive number of individual human breast cells. We then sequenced every individual cell outputting unstructured sequence data. Using computational approaches, we sequenced these cells into a digital format, resulting in the largest and most comprehensive digital characterisation of cell types present in the human breast. This builds the foundation of our machine learning model.

Aligned with our IntercepTx machine learning approach, the digital characterisation of the breast cell types allows us to quantify the cellular changes in pre-cancerous progression for each cell. We use these insights to help pharmaceutical companies to assess the efficacy of cancer prevention therapeutics. We do that by offering a service in pre-clinical and clinical settings to assess to what extent each drug impacts the pre-cancerous stage of samples.

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