Models and Data in Biomedicine: What's Real and What's Noise? And, Why Should We Care?
Monday, March 14, 2016 1:00pm - 2:00pm EST | Lipsett Auditorium, Building 10, NIH Main Campus, Bethesda, MD
Speaker: Carlos Bustamante, Ph.D., Stanford University
Abstract: If you think of a scatterplot of data overlaid with a model for the data and ask practitioners from different fields, “what’s noise and what’s real?” the answers may surprise you. To a biologist, the data will almost surely be “what’s real” and the model is a poor approximation to the “truth.” To a physicist, the model is probably “what’s real” and the data is just a noisy realization of an underlying true physical process that we are attempting to study. As we think about the biomedical data enterprise in the 21st century and the massive amounts of data we generate (and want to analyze!), we need to support multiple world views and have guidance on how to translate noisy data and noisy models into actionable information. Dr. Bustamante's presentation will draw upon several examples from Population Genetics (a field very rich in theory) and Genomics (a field not so rich in theory and much more data driven) to illustrate these points. It will also touch upon reproducible research and the question of how funding agencies need to support ecosystems for collaborative research including data producers, consortia, and so called "research parasites” that may want to use the data in ways that go beyond what the original experimental designers envisioned.
About the Speaker: Carlos Bustamante is a population geneticist whose research focuses on analyzing genome-wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. From 2002-2009, he was on the faculty at Cornell University in the Departments of Statistical Sciences and Biology Statistics and Computational Biology, where he was promoted to full professor in 2008. Since 2010, he has been on the faculty in the Department of Genetics at the Stanford University School of Medicine.
He has received multiple honors and awards including a Marshall-Sherfield Fellowship (2001-2), the Sloan Research Fellowship (2007), and a John D. and Catherine T. MacArthur Fellowship (2010). He has trained over 50 post-doctoral fellows and graduate students as primary advisor and co-authored over 130 papers. Much of his research is located at the interface of computational biology, mathematical genetics, and evolutionary genomics.
His most current research focuses on human population genomics and global health, including developing statistical, computational, and genomic resources for enabling trans- and multi-ethnic genome-wide association and medical sequencing studies of complex biomedical traits. He is one of the Principal Investigators of the recently announced $25M ClinGen project to build the country's National Database of Clinically Relevant Genomic Variants.
Additional Event Details:
Event Page:NIH invites you to participate in our second annual celebration of Pi Day, with a day of events and activities celebrating the intersection between the quantitative and biomedical sciences. Pi Day is an annual celebration of the irrational number Pi, 3.14..., on March 14. On Pi Day and every day, NIH recognizes the importance of building a diverse biomedical workforce with the quantitative skills required to tackle future challenges. For more information, visit:https://datascience.nih.gov/PiDay2016
Attending the seminar:This is a public event at the National Institutes of Health. All individuals interested in the seminar may attend. If this will be your first time visiting the NIH, we strongly encourage you to review the visitor information at http://www.nih.gov/about/visitor/index.htm and allow extra time for security and transit. Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact Kristan Brown, at Kristan.Brown@nih.gov or 301-402-9818. Requests should be made at least 5 business days in advance of the event.