Peter Song, Professor
Department of Statistics
School of Public Health
University of Michigan
Statistical Approaches to Addressing Data Science Challenges in
Epigenetic Aging Research
Abstract: DNA methylation (DNAm) has emerged as a key source of omics data for assessing biological age, offering a wealth of genetic markers that reflect cellular changes influenced by social and environmental factors. Epigenetic age can be estimated through predictive models known as epigenetic clocks, which rely on high-dimensional data analytics. However, current epigenetic age calculators face significant limitations as DNAm data collection technology rapidly advances. In this talk, I will present statistical approaches to tackle several critical challenges, including: (i) refining epigenetic clocks with higher-resolution DNAm data using convolutional neural networks, (ii) quantifying prediction uncertainty using conformal prediction techniques to address increasing variability over aging, and (iii) conducting functional regression to understand potential influence of physical activity on biological age. This presentation will integrate both statistical methodologies and algorithmic solutions, demonstrated through real-world data applications.
Bio: Dr. Song is Professor of Biostatistics at the School of Public Health in the University of Michigan, Ann Arbor. He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996. He has published over 230 peer-reviewed papers and graduated 26 PhD students and trained 6 postdoc research fellows. Dr. Song's current research interests include data integration, distributed inference, high-dimensional data analysis, longitudinal data analysis, mediation analysis, spatiotemporal modeling, and applications in medicine and public health. He collaborates extensively with researchers from nutritional sciences, environmental health sciences, chronic diseases, infectious disease, aging and nephrology. He is IMS Fellow, ASA Fellow and Elected Member of the International Statistical Institute. Dr. Song now serves as Associate Editor of the Journal of American Statistical Association, the Annals of Applied Statistics, and the Journal of Multivariate Analysis.
DATE: Wednesday, October 30, 2024, 4:00 PM, AUST 202
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m5921df6237e0951215ee7c351169e2ef
Coffee will be served at 3:30 in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu