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

While there has been an extensive effort in identifying pathogenic mutations in patients’ genomes, current methods still cannot efficiently prioritize the true pathogenic mutations in patients. We showed that by using extensive annotations it is possible to cluster mutations by disease groups. We aim to deep neural network (aka “deep learning”) classifier to efficiently and automatically prioritize pathogenic mutations in patients’ genomes, by considering the disease of the patient, train based on extensive annotations at the variant-, gene- and pathway-levels, and separate the training sets by disease groups and high-quality non-trivial neutral genetic variants.

Publications

Publications:137

Selected Publications

View All Publications

Industry Relationships

Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device, biotechnology companies, and other outside entities to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their outside financial relationships.

Dr. Itan has not yet completed reporting of industry relationships or has no industry relationships to report.

Mount Sinai’s faculty policies relating to faculty collaboration with industry are posted on our website. Patients may wish to ask their physician about the activities they perform for companies.