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Statistical and Computational Algorithms for Analyzing Biobank Data

Computational Genomics Summer Institute CGSI via YouTube

Overview

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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.

Syllabus

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

Taught by

Computational Genomics Summer Institute CGSI

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