Yuval Itan, PhD

About Me

Dr. Yuval Itan is an Associate Professor in the Department of Genetics and Genomic Sciences, a core member of The Charles Bronfman Institute for Personalized Medicine, and a member of Mindich Child Health and Development Institute, at the Icahn School of Medicine at Mount Sinai in New York City.

The main focus of the Itan lab is investigating human disease genomics for enhancing precision medicine, by developing new machine learning and computational methods to detect disease-causing variants and genes in next generation sequencing data of patients, and by performing cases-controls genome- and phenome-wide studies of patient cohorts across diverse human populations to identify new genetic etiologies of human diseases.

The Itan lab applies and combines diverse approaches across computer science and biology, including machine learning, natural language processing, bioinformatics, statistical genomics, modelings and simulations, and population genetics.

Itan lab webpage

Language

English

Position

ASSOCIATE PROFESSOR | Artificial Intelligence and Human Health, ASSOCIATE PROFESSOR | Genetics and Genomic Sciences

Research Topics

Bioinformatics, Biomedical Informatics, Biomedical Sciences, Biostatistics, Cardiovascular, Clinical Genomics, Computational Biology, Computer Simulation, Coronavirus, Epidemiology, Evolution, Gastroenterology, Gene Discovery, Genetics, Genomics, Immune Deficiency, Infectious Disease, Inflammatory Bowel Disease (IBD), Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Neural Networks, Obesity, Parkinson's Disease, Personalized Medicine, Proteomics, Sequence Alignment, Systems Biology, Technology & Innovation, Theoretical Biology, Translational Research

Multi-Disciplinary Training Areas

Artificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS]

Education

BSc, Bar-Ilan University

PhD, University College London

Postdoc, The Rockefeller University

Research

Different human populations display varying genomic architectures, that are likely to result in population-specific disease-causing mutations, genes and pathways. We currently investigate this concept with Ashkenazi Jewish (AJ) inflammatory bowel disease (IBD) patients from the IBD genetics consortium (IBDGC) whole exome sequencing data, that we identify by admixture and principal component analyses (PCA). We perform a gene burden analysis of cases vs controls, focusing on high-impact rare genetic variants. We use PheWAS to further validate our results. We aim to then extend the analysis to other human populations (Hispanic, African American and European) for identifying population-specific IBD genomic signals.

Itan lab webpage

Publications

Publications:133

Selected Publications

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