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BALTIMORE, MD, September 5, 2018 – Personal Genome Diagnostics, Inc. (PGDx), a leader in cancer genomics, today announced that its machine learning based technology, CerebroTM, outperformed existing methods to identify tumor-specific, or somatic mutations, enabling more accurate NGS clinical test results. The study, published in Science Translational Medicine, was conducted using analysis of data from 1,368 samples.

“Increasingly, NGS diagnostic tests are being used to identify genetic alterations that help oncologists make decisions with their patients about the potential effectiveness of therapies. However, different NGS approaches have varying results, calling into question the ability of NGS to detect real mutations,” said Sam Angiuoli, PhD, Chief Information Officer at PGDx. “We know it is absolutely critical to get the right answer for patients, so we pioneered the development of automated NGS software that incorporates machine learning strategies to improve the accuracy of somatic mutation detection. As this study reveals, our approach yields better results compared to alternatives and highlights the importance of combining state-of-the-art software and data science in genomic testing.”

Cerebro’s machine learning approach analyzes a wide variety of characteristics to assess whether any given identified mutation is real. The accuracy of the Cerebro technology was compared to existing

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