Noam Beckmann

Noam Beckmann, PhD

About Me

Dr. Beckmann is Assistant Professor in the Division of Data Driven & Digital Medicine at the Icahn School of Medicine at Mount Sinai as well as the Director of Data Sciences and founding member for the Mount Sinai Clinical Intelligence Center (MSCIC). His research interests are focused on the integration of genetic and large-scale multi-omics datasets into meaningful networks models that can inform on specific biological questions, both on disease and health states, to identify core nodes and subnetworks that can be informative to disease etiology and potential therapeutic targets. Dr. Beckmann's work has contributed to the identification of master regulators of complex diseases and traits such as Alzheimer’s disease, stemness heterogeneity of induced pluripotent stem cells (iPSCs), peanut allergic reactions, cancer, coronary artery disease, schizophrenia and chronic fatigue syndrome, to the description of new method to identify cancer driver pathways, and to the identification of molecular processes involved in the host response to COVID-19, multisystem inflammatory syndrome in children and the post-acute sequelae to SARS-CoV-2 infection. His experience ranges from processing of raw genetic data and RNA sequencing data, to analysis of genetic variants, gene expression, protein expression and their association with each other and/or to clinical traits, to network modeling including Bayesian network regulatory frameworks and other machine learning tools, to large collaboration and project management. During the COVID-19 surge, Dr. Beckmann co-created and had a leading role in the design of the Mount Sinai COVID-19 Biobank and was the lead for analyses of all data generated as part of it, encompassing molecular and extended clinical data derived from patients’ EHR. This work now continues to expand with his position of Director of Data Sciences for MSCIC. Dr. Beckmann is excited to develop new approaches to perform big data analyses and integration of multiple layers of omics, from genome to phenome with everything in between, with the goal of addressing important biological problems and eventually narrow down on disease mechanisms and clinically actionable targets.

Lab website:

ASSISTANT PROFESSOR | Medicine, Data Driven & Digital Medicine
Research Topics

Alzheimer's Disease, Bioinformatics, Biomedical Informatics, Biomedical Sciences, Brain, Computational Biology, Coronavirus, Gene Expressions, Gene Regulation, Genetics, Genomics, Human Genetics and Genetic Disorders, Infectious Disease, Mathematical and Computational Biology, Neuroscience, Personalized Medicine, Proteomics, Psychiatry, RNA, SARS Virus, Schizophrenia, Systems Biology

Multi-Disciplinary Training Areas

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