Using a beta version of Watson for Genomics technology, the proof-of-concept study compared multiple techniques used to analyze genomic data from a brain cancer patient's tumor cells and normal healthy cells. In the study, Watson was able to provide clinically actionable insights within 10 minutes, compared to the 160 hours of human analysis and curation that was needed to arrive at similar conclusions.
In addition, the study showed that whole genome sequencing (WGS) identified more clinically actionable mutations compared to the current standard method of examining a limited subset of genes. While WGS currently requires significantly more manual analysis, the study's results suggest that combining this method with AI could help doctors identify potential therapies from WGS for more patients in less time.
The beta version of Watson for Genomics processed abstracts and, in some cases, full text articles from PubMed - a comprehensive source of more than 27 million citations for biomedical literature. With this information, researchers and bioinformatics experts from the New York Genome Center (NYGC) and Watson collaborated to identify gene alterations that could be therapeutically targeted.
“This study documents the strong potential of Watson for Genomics to help clinicians scale precision oncology more broadly," says Vanessa Michelini, Watson for Genomics Innovation Leader, IBM Watson Health. "Clinical and research leaders in cancer genomics are making tremendous progress towards bringing precision medicine to cancer patients, but genomic data interpretation is a significant obstacle, and that’s where Watson can help."
The study was part of the NYGC's ongoing efforts to advance the use of next-generation sequencing - particularly WGS - in precision medicine. The NYGC and its founding member institutions are conducting additional studies involving Watson, including an ongoing study that involves DNA and RNA from a larger cohort of glioblastoma patients, and a study of 200 patients with different types of cancer.
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