USING MOLECULAR BIOLOGY AND MACHINE LEARNING TO DETECT CANCER

USING MOLECULAR BIOLOGY AND MACHINE LEARNING TO DETECT CANCER

Freenome is known for seeing what humans cannot see. By decoding cell-free biomarker patterns of once-unthinkable complexity, Freenome’s blood tests are powered by its multi-omics platform and designed to detect cancer with the help of machine learning and molecular biology at its earliest stages to help clinicians optimize treatments and the next generation of precision therapies.

 

How Does the Multiomics Platform Work?

By training on thousands of cancer-positive blood samples, Freenome’s multi-omics platform learns which biomarker patterns signify cancer’s type and effective treatment pathways. Training on healthy samples helps experts to establish what a normal composition of cell-free biomarkers should look like. This unique concept of Freenome makes the company stand out among all.

What Does the Multiomics Platform Detect?

Freenome’s multiomics platform detects key biological signals from a routine blood draw. The platform integrates assays for cell-free DNA, methylation, and proteins with advanced computational biology and machine learning techniques to understand additive signatures for early cancer detection.

This strategy incorporates a multidimensional view of both tumor- and non-tumor-derived (e.g. immune) signatures that enable the early detection of cancer, instead of relying only on tumor-derived markers, which may miss the early signs of cancer.

Mapping the Multiomics of Blood

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