Scientists at the Icahn School of Medicine at Mount Sinai have developed a novel artificial intelligence (AI) tool, V2P (Variant to Phenotype), that identifies disease-causing genetic mutations and predicts the associated disease types. This advancement aims to enhance genetic diagnostics and facilitate the discovery of treatments for complex and rare diseases.
Published in Nature Communications, the research led by Dr. Yuval Itan and Dr. Avner Schlessinger demonstrated that V2P outperforms existing methods in identifying pathogenic variants from patient sequencing data. This tool addresses a critical gap in current genetic analysis, which often fails to distinguish between pathogenic variants linked to different diseases.
By utilizing advanced machine learning, V2P connects genetic variants with their likely phenotypic outcomes, thereby predicting how specific mutations may influence health. This capability not only streamlines genetic diagnostics but also guides researchers in prioritizing genes for further investigation, ultimately paving the way for precision medicine tailored to individual genetic profiles.
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