Predicting Viral Infection from High-Dimensional Biomarker Trajectories

Minhua Chen, University of Chicago
Host: Suchi Saria

There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection.

Speaker Biography

Minhua Chen received his Ph.D. degree from Duke University in May 2012 with Profs. Lawrence Carin and David Dunson working on Bayesian and Information-Theoretic Learning of High Dimensional Data. Currently he is working on statistical machine learning problems at University of Chicago in collaboration with Prof. John Lafferty. His research interests broadly span machine learning, signal processing and bioinformatics.