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Using DNA repair and machine learning to improve cancer risk prediction

Sep 7, 2020

The project uses a protein marker recruited to the site of DNA damage and bound with a fluorescent antibody to analyze DNA repair over time following irradiation. The decay, or not, in the fluorescence signal is indicative of the efficiency of normal DNA repair by the cells. Bacon laboratory concludes with an exciting prospect for the use of machine learning allied to cutting-edge imaging flow cytometry. “It’s opening up avenues of research that were not previously plausible,” he says. He describes the “vast sea of information” that emerges from the Luminex Amnis ImageStream X Mk II and how machine learning provides the means of navigating that sea. New knowledge is likely to abound, as Bacon asserts: “Just in this one project, I could write four different papers on the data which has come out from it.”

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Luminex offer a comprehensive range of innovative flow cytometers, including the Imaging flow cytometers.

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Combine the speed and statistical power of flow cytometry with microscopy

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