Yuval Itan
@itanlab.bsky.social
Human disease genomics, precision medicine and machine learning. Associate Professor at @IcahnMountSinai
Reposted by Yuval Itan
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
www.nature.com/articles/s41...
www.v2p.ai
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...
www.nature.com
December 16, 2025 at 12:49 PM
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
www.nature.com/articles/s41...
www.v2p.ai
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
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
December 15, 2025 at 6:02 PM
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
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
Reposted by Yuval Itan
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
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.
medicalxpress.com
December 15, 2025 at 10:00 AM
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
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.
Congrats to Ece Kars who led this work, and thanks to Bruce Gelb & the PCGC consortium for the collaboration.
February 20, 2025 at 4:41 PM
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.
Congrats to Ece Kars who led this work, and thanks to Bruce Gelb & the PCGC consortium for the collaboration.