Healthcare (Commonwealth Union) – As artificial intelligence (AI) further advances, the potential for its applications in the healthcare industry continue become more and more significant. The ability to gather data and predict the outcomes via analysis at a much faster rate show the potential advanced diagnostics and treatments.
Scientists at the Icahn School of Medicine at Mount Sinai have created a new AI system that goes beyond spotting harmful genetic mutations by also forecasting the kinds of diseases those mutations are likely to cause.
Known as V2P (Variant to Phenotype), the approach aims to speed up genetic diagnosis and support the search for new therapies, particularly for rare and complex conditions. The research was published online on December 15 in Nature Communications.
Most existing genetic tools can judge whether a mutation is damaging, but they cannot say what disease it may lead to. V2P addresses this limitation by applying advanced machine-learning techniques to connect specific genetic variants with their probable phenotypic effects—in other words, the diseases or traits a mutation may produce—offering insight into how an individual’s DNA might affect their health.
The lead author David Stein, PhD, who recently completed his doctoral work in the laboratories of Yuval Itan, PhD, and Avner Schlessinger, PhD indicated that instead of sorting through thousands of possible genetic variants, their method helps them focus on the changes most likely to explain a patient’s symptoms.
They further pointed out that by identifying not just whether a variant is harmful but also the type of disease it may cause, they can make genetic analysis faster and more precise.
To build the model, the researchers trained V2P on an extensive dataset containing both disease-causing and harmless genetic variants, along with detailed disease information. When tested on real, anonymised patient data, the tool frequently placed the actual disease-causing mutation within its top ten predictions, demonstrating its promise for making genetic diagnostics more efficient.
“Beyond diagnostics, V2P could help researchers and drug developers identify the genes and pathways most closely linked to specific diseases,” explained Dr. Schlessinger, co-senior and co-corresponding author, Professor of Pharmacological Sciences, and Director of the AI Small Molecule Drug Discovery Center at the Icahn School of Medicine at Mount Sinai. “This can guide the development of therapies that are genetically tailored to the mechanisms of disease, particularly in rare and complex conditions.”
While V2P currently groups genetic mutations into broad classes—such as neurological disorders or cancer—the research team plans to further develop the system so it can forecast more precise disease outcomes and be combined with additional datasets to aid drug development.
The advance marks progress toward precision medicine, where therapies are tailored to an individual’s genetic makeup. By linking specific genetic variants to their probable disease effects, V2P could streamline diagnosis for clinicians and help researchers pinpoint new targets for treatment, the scientists explain.
Dr. Itan, co-senior and co-corresponding author and Associate Professor of Artificial Intelligence and Human Health, and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai indicated that V2P offers deeper insight into how genetic alterations lead to disease, with significant benefits for both scientific research and patient care.
He further pointed out that by mapping particular variants to the kinds of conditions they are most likely to cause, they can more effectively identify which genes and biological pathways deserve closer study. Dr. Itan then indicated that this permits them to move faster from understanding disease mechanisms to developing potential therapies, and ultimately to personalising treatment based on an individual’s unique genomic profile.
The paper was titled “Expanding the utility of variant effect predictions with phenotype-specific models”
