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Stated UCI’s Sina Faezi, “Through a careful feature engineering and bespoke machine learning algorithm written in our lab, we were capable of pinpoint these differences,” and easily distinguish each time the machine generated A, G, C, or T. When the researchers used software program to research the AGCT patterns they obtained via the recordings, they identified the correct kind of DNA with 86% accuracy, then boosted accuracy to nearly one hundred% by working it by way of extra DNA sequencing software program.

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