Yuval Itan | Mount Sinai

About Me

Dr. Yuval Itan is an Associate Professor in the Department of Genetics and Genomic Sciences and a core member of The Charles Bronfman Institute for Personalized Medicine, 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 computational methods to detect disease-causing mutations and genes in next generation sequencing data of patients, and by performing cases-controls studies of patient cohorts 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, Evolution, 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, 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]

About Me

Dr. Yuval Itan is an Associate Professor in the Department of Genetics and Genomic Sciences and a core member of The Charles Bronfman Institute for Personalized Medicine, 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 computational methods to detect disease-causing mutations and genes in next generation sequencing data of patients, and by performing cases-controls studies of patient cohorts 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, Evolution, 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, 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.