Integrating Patient Level Multi-omics Data Using Deep Learning Models
Explore cutting-edge approaches to integrating patient-level multi-omics data using deep learning models in this 50-minute workshop talk by Simon Rasmussen from the NNF Center for Protein Research at the University of Copenhagen and the NNF Center for Genomic Mechanisms of Disease at the Broad Institute. Delve into supervised learning techniques for predicting patient outcomes and discover the power of EIR for large-scale genomics data analysis. Examine methods for integrating genomics and biomarkers, and learn about unsupervised deep learning approaches, including autoencoders, for data integration in a T2D cohort. Gain valuable insights into latent representation and future perspectives in multi-modal data integration, essential for advancing personalized medicine and genomic research.