Statistical and Computational Algorithms for Analyzing Biobank Data

Explore statistical and computational algorithms for analyzing biobank data in this comprehensive lecture from the Computational Genomics Summer Institute (CGSI) 2024. Delve into robust methods for estimating within-subject variances from intensive longitudinal data, as presented in the WiSER approach. Examine techniques for conducting genome-wide association studies (GWAS) on longitudinal trajectories at a biobank scale. Discover a platform for phenotyping disease progression and associated longitudinal risk factors in large-scale electronic health records (EHRs), with a focus on incident diabetes complications in the UK Biobank. Gain insights into cutting-edge statistical and computational approaches that address the challenges of analyzing complex biobank datasets, enabling more accurate and efficient analysis of longitudinal health data.

Hua Zhou | Statistical and Computational Algorithms for Analyzing Biobank Data | CGSI 2024