Home Systems biology Using molecular biology and machine learning to detect cancer

Using molecular biology and machine learning to detect cancer

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

How does the Multiomics platform work?

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

What does the Multiomics platform detect?

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

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

Mapping blood multiomics

Mapping of blood multiomics based on cfDNA, cfRNA and proteins.

cfDNA: This process provides information on the immune response and tumor heterogeneity.

cfRNA: Reveals changes in gene expression in the body associated with tumor formation.

Protein: Provides a higher order dimension to immunogenic and oncogenic signals.

By combining deep expertise in molecular biology and advanced computational techniques such as artificial intelligence, machine learning, etc. actionable information on health systems to operationalize a feedback loop between care and science.

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