ML-Enabled Genetic Association Studies of High-Content Phenotypes

Watch a research presentation from the Computational Genomics Summer Institute that explores machine learning applications in genetic association studies of high-content phenotypes. Delve into cutting-edge methodologies presented by Francesco Paolo Casale, drawing from multiple published works including HistoGWAS framework for automated genetic analysis of tissue phenotypes, differentiable Mendelian randomization for disease risk predictions, and mixed models with multiple instance learning. Learn about innovative approaches combining Gaussian Process Prior Variational Autoencoders and efficient set tests for analyzing correlated genetic traits. Gain insights into how artificial intelligence and machine learning are advancing the field of computational genomics and enabling more sophisticated analysis of complex biological data.