Yuval Itan | Mount Sinai

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 | 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

Gain-of-function (GOF) and loss-of-function (LOF) mutations in the same gene result in different diseases and require different treatment. We aim to develop the first computational method to efficiently predict if a mutation is GOF, LOF or neutral by: (1) creating the first extensive GOF and LOF database by extracting data with natural language processing (NLP) algorithm on abstracts of known pathogenic mutations; (2) applying statistical and feature selection approach to detect protein-level and gene-level features that best differentiate GOF from LOF and neutral mutations; and (3) developing a Random Forest classifier and a public server to predict the functional consequence of mutations. We use Phenome-Wide Associations (PheWAS) on Mount Sinai’s BioMe resource for validating our resource and detect novel GOF/LOF phenotypes.