Are you interested in learning more about biological aging?

Join Dr. Huxia (Judy) Wang, professor and chair of the Department of Statistics, William Marsh Rice Trustee Professor in Data Science, and member of the Ken Kennedy Institute at Rice University, for a presentation on a novel approach to estimating biological age.

Biological age aims to measure a person's aging status more accurately than chronological age, yet different epigenetic clocks often produce varying age estimates for the same individual. Dr. Wang will discuss a rank-based framework that combines multiple epigenetic clocks to create a more reliable measure of biological aging. This approach uses comparisons with age-matched peers and advanced statistical methods to generate consensus aging estimates without relying on restrictive assumptions.

This presentation will highlight preliminary findings from simulation studies and DNA methylation age data from the ELEMENT study, as well as ongoing research exploring calibration, identifiability and weighting strategies for improving biological age estimation.