Yuval Itan's Avatar

Yuval Itan

@itanlab.bsky.social

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

161 Followers  |  480 Following  |  6 Posts  |  Joined: 19.02.2025  |  1.3076

Latest posts by itanlab.bsky.social on Bluesky

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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...

Check out our new paper introducing V2P โ€” 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

16.12.2025 12:49 โ€” ๐Ÿ‘ 6    ๐Ÿ” 1    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0

Lastly, we dissect the biology of pathogenic variants by disease domain using feature selection: discriminative annotations for each top-level HPO group, revealing which signals are shared across domains and which are phenotype-specific.

15.12.2025 18:04 โ€” ๐Ÿ‘ 3    ๐Ÿ” 0    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0

Additionally, we show in real patients' exomes that the known causal variant has a median rank of #2 when prioritizing with the V2P phenotype-specific score(s) matching the patientโ€™s phenotype.
@casanovalab.bsky.social

15.12.2025 18:04 โ€” ๐Ÿ‘ 2    ๐Ÿ” 0    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 0

V2P webserver: v2p.ai

15.12.2025 18:03 โ€” ๐Ÿ‘ 1    ๐Ÿ” 0    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 0
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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

15.12.2025 18:02 โ€” ๐Ÿ‘ 4    ๐Ÿ” 1    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 2
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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.

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

15.12.2025 05:00 โ€” ๐Ÿ‘ 4    ๐Ÿ” 2    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0
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itan-lab / Digenic Analysis ยท GitLab GitLab.com

In this project, Ece developed a robust digenic case-control association framework, which we highly recommend applying to various disease groups to uncover missing heritability. Guideline for the process: gitlab.com/itan-lab/dig...

20.02.2025 16:42 โ€” ๐Ÿ‘ 3    ๐Ÿ” 0    ๐Ÿ’ฌ 0    ๐Ÿ“Œ 0
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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.

20.02.2025 16:41 โ€” ๐Ÿ‘ 6    ๐Ÿ” 1    ๐Ÿ’ฌ 1    ๐Ÿ“Œ 0

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