Whole Genome Methylation Patterns as a Biomarkers for EHR Imputation

Published on ● Video Link: https://www.youtube.com/watch?v=bmE65FtrGVw



Duration: 31:26
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Eran Halperin (Optum Labs and UCLA)
https://simons.berkeley.edu/talks/whole-genome-methylation-patterns-biomarkers-ehr-imputation
Computational Challenges in Very Large-Scale 'Omics'

Diagnosis and prediction of health outcomes using machine learning has shown major advances over the last few years. One of the major challenges remaining is the sparsity of electronic health records data, which often requires an imputation step. Genomic data can potentially be used to improve our ability for imputation; indeed, polygenic risk scores using genetic data have been heavily studied in the past few years. In this talk, I will present approaches for imputation using DNA methylation, and compare those predictions to polygenic risk scores and to traditional EHR imputation approaches.




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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Computational Challenges in Very Large-Scale 'Omics'
Eran Halperin