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Yuval Itan

@itanlab.bsky.social
181 followers 480 following 6 posts

Human disease genomics, precision medicine and machine learning. Associate Professor at @IcahnMountSinai

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Reposted by Yuval Itan
Avner Schlessinger @schlessingerlab.bsky.social · 16/12/2025
Check out our new paper introducing V2P — a method that predicts both variant pathogenicity and disease phenotype across 23 HPO categories. With @itanlab.bsky.social, David Stein, and many other great collaborators www.nature.com/articles/s41... www.v2p.ai
nature.com
Expanding the utility of variant effect predictions with phenotype-specific models - Nature Communications
V2P predicts variant pathogenicity conditioned on disease phenotypes across top-level Human Phenotype Ontology categories. This approach shows promise for phenotype-specific estimation of variant effe...
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Yuval Itan @itanlab.bsky.social · 15/12/2025
V2P (variant-to-phenotype) is live: nature.com/articles/s41... To our knowledge, first genomewide SNVs+indels model jointly predicting pathogenicity + disease domain (23 HPO groups; e.g. cardiac/immune/metabolic). Great work by David Stein in collaboration with @schlessingerlab.bsky.social
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Reposted by Yuval Itan
Science X / Phys.org @sciencex.bsky.social · 15/12/2025
A new AI tool links genetic mutations to specific disease types, enhancing the speed and accuracy of genetic diagnostics and supporting the discovery of targeted treatments for complex conditions. doi.org/hbfn92
medicalxpress.com
New AI tool identifies not just genetic mutations, but the diseases they may cause
Scientists at the Icahn School of Medicine at Mount Sinai have developed a novel artificial intelligence tool that not only identifies disease-causing genetic mutations but also predicts the type of disease those mutations may trigger.
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Yuval Itan @itanlab.bsky.social · 20/02/2025
Our new publication on the digenic architecture (two causative genes in a single patient) of congenital heart disease is now online: www.sciencedirect.com/science/arti... Congrats to Ece Kars who led this work, and thanks to Bruce Gelb & the PCGC consortium for the collaboration.
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