Purvesh Khatri, Ph.D., will present “Adventures of a Data Parasite: Accelerating Clinical Translation Using Heterogeneity in Public Data” at the monthly Data Sharing and Reuse Seminar on Sept. 10 at 12 EDT. Khatri is an associate professor at the Institute for Immunity, Transplantation and Infection and in the Division of Biomedical Informatics Research, Department of Medicine, Stanford University.
About the Seminar
This talk will focus on how biological, clinical, and technical heterogeneity across publicly available independent datasets can lead to identification of disease signatures that are diagnostic, prognostic, therapeutic, and mechanistic across a broad spectrum of diseases including infections, autoimmune diseases, cancer, organ transplant, and vaccination. Khatri will also discuss how biological and technical heterogeneity in publicly available data can be leveraged to make translational medicine better, faster, cheaper, and more generalizable.
About the Speaker
Khatri is an electronics and communications engineer turned software engineer turned computational systems immunologist. His research focuses on developing novel methods for leveraging heterogeneity present across independent cohorts to better understand human immune system for developing novel diagnostics and therapies for inflammatory diseases including autoimmune and infectious diseases, organ transplant and cancer.
About the Seminar Series
The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Erin Walker at 301-827-9655 or the Federal Relay Service at 800-877-8339. Requests should be made at least five days in advance of the event.
The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight exemplars of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.