WEDNESDAY, May 1, 2024 (HealthDay News) — A random-forest, machine-learning classifier, artificial intelligence-Model organism Aggregated Resources for Rare Variant ExpLoration (AI-MARRVEL), achieves superior accuracy compared with existing methods for genetic diagnosis, according to a study published online in the May issue of NEJM AI.
Dongxue Mao, Ph.D., from the Baylor College of Medicine in Houston, and colleagues evaluated AI-MARRVEL (AIM) using data from patients diagnosed with genetic disorders from three independent cohorts.
The researchers reported that AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases versus benchmarked methods, across the three real-world cohorts. Using a confidence metric to better identify diagnosable cases from the unsolved pools accumulated over time, AIM achieved a precision rate of 98 percent and identified 57 percent of diagnosable cases out of a collection of 871 cases. After being fine-tuned for targeted settings, including recessive disorders and trio analysis, AIM performance improved. AIM also showed potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.
“AIM achieved superior accuracy compared with existing methods for genetic diagnosis,” the authors write. “We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes.”
The Baylor College of Medicine and Miraca Holdings Inc. have formed a joint venture with shared ownership and governance of Baylor Genetics, which performs genetic testing and derives revenue.
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