Please join Mahdi Moqri, PhD MBA, Joint Research Fellow from Stanford University and Harvard University, and Jesse Poganik, PhD, Instructor in Medicine from Brigham and Women’s Hospital, Harvard Medical School for a dynamic webinar.
Machine learning models based on Omics data can predict biological age but often do not offer biologically meaningful or clinically actionable insights. Many research groups have been working to address this challenge during the past few years. Drs. Moqri and Poganik present a brief review of recent advances in this area developed by the members of the Biomarkers of Aging Consortium, including the PRC2 Clock, the Causal Clock, the OMICmAge Clock, biological applications of the clocks, and computational tools including Biolearn and Clockbase.