Speaker Profile
Ph.D., Associate Professor of Medicine, Harvard
Biography
Dr. Libermann is an internationally recognized translational investigator with a strong track record in precision medicine, biomarker discovery and translational studies of immunological diseases and cancer. Dr. Libermann’s lab is focused on applying multi-omics approaches to identify proteins or genes that may be exploited as biomarkers or targets for therapeutic intervention, starting with his seminal discovery of EGF receptor gene amplifications in glioblastomas. Dr. Libermann has been at the forefront of the next generation of proteomics and plays a central role in driving biomarker discovery to the next level. Libermann’s group used SOMAscan, a modified aptamer-based high multiplex immunoassay platform, to discover and validate diagnostic, predictive, and prognostic biomarkers for various diseases. Using the latest in machine learning, Libermann’s group has developed predictor models for applications in disease diagnosis, prediction of therapeutic response, monitoring of disease progression, and outcome prediction. Dr. Libermann received his PhD in Immunology in 1986 from the Weizmann Institute of Science and Technology, Rehovot.
Clinical Dx Showcases:
Harvard
Beth Israel Deaconess Medical Center (BIDMC), an affiliated teaching hospital of Harvard Medical School, brings together academic medical centers and teaching hospitals in a shared mission to advance the science and practice of medicine through groundbreaking research and education.
Next Generation Proteomics Driving Precision Medicine
Proteomics has emerged as the next frontier in precision medicine. I will present applications of the latest, cutting edge proteomics tools such as SOMAscan for disease biomarker discovery and how machine learning enables development of high accuracy predictor models.
Session Abstract – PMWC 2020 Silicon Valley
Physicians and patients both benefit from big health data to achieve proactive health. This session talks about engaging both physicians and patients to utilize health data in order to manage patient's own health. It touches upon the educational aspects that need to be considered and how to actively engage both patients and physicians.
