Machine Learning for Biobank-Scale Genomic Data - CGSI 2022
Explore machine learning techniques for analyzing Biobank-scale genomic data in this comprehensive conference talk from the Computational Genomics Summer Institute. Delve into key inference problems and the genetic architecture of complex traits, focusing on variance components models and efficient estimation methods. Learn about the Randomized HE-regression (RHE) approach for accurate and scalable analysis of large-scale genomic data. Examine insights gained from applying RHE to the UK Biobank, including dominance deviation effects, gene-environment interactions, and gene-gene interactions. Discover how Random Fourier Features (RFF) can be used to address higher-order effects and tackle the challenge of missing data in Biobanks. Gain valuable knowledge on cutting-edge machine learning applications in genomics, supported by related research papers on variance components analysis, dominance deviation effects, and population structure inference in biobank-scale data.