Statistics Seminar - 05/01/26

Abstract:

In this talk, I will present a resampling-based pseudo-training framework for genomic prediction that enables model development using only summary-level data. We show that generating pseudo-training and validation statistics from summary results achieves asymptotic equivalence to conventional training while avoiding the need for individual-level datasets. Simulations and real data applications suggest that pseudo-training performs comparably to standard approaches with large datasets and substantially better when tuning data are limited. We highlight two platforms built on this framework: PennPRS (https://pennprs.org/), a cloud-based computing infrastructure supporting large-scale, no-code polygenic risk score training with purely summary data resources, and GCB-Hub (https://www.gcbhub.org/), which applies pseudo-training to proteome-wide association studies for protein-disease mapping and drug discovery. Together, these advances demonstrate how resampling-based pseudo-training methods can broaden accessibility, scalability, and impact of genomic prediction across diverse biomedical research settings.

About the Speaker:

Dr. Bingxin Zhao is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, with a secondary appointment in Department of Medicine, Perelman School of Medicine. His research focuses broadly on statistics, AI in science and medicine, and brain-body connections (https://www.bingxinzhao.com/).

His research has been recognized by the ICSA Outstanding Young Researcher Award in 2024 (https://www.icsa.org/awards/icsa-awards-recipients/) and the IMS Tweedie New Researcher Award in 2025 (https://imstat.org/ims-awards/ims-awards-recipients/).

Note:

Contact Prof. JZ at jzhang@math.msstate.edu for additional information.