Therapeutic genome editing and its need for artificial intelligence
Friday, 14.1.22, 12:00-13:00, online: Zoom
Deep generative approaches for omics data: interpretability and sample-size constraints
Friday, 21.1.22, 12:00-13:00, online: Zoom
Deep generative models (DGMs) are promising tools, e.g., for learning latent structure in high dimensional omics data such as single-cell RNA-Seq data as well as for generating synthetic observations, e.g. for securely sharing single nucleotide polymorphism (SNP) data. Here I address the interpretability of DGMs, specifically by showing how to\nlink latent space information with observed variables (e.g. expression levels of genes). In addition I address the performance of DGMs under sample size constraints which are frequently observable when working with omics data in the biomedical context.
Friday, 28.1.22, 12:00-13:00, online: Zoom