Predicting Single Cell Expression from Multiplex-Immunofloresent Imaging and Bulk-Count Data
Spatial omics allows for the investigation of the Tumor Immune Microenvironment (TIME). Stratifying patients by their TIMEs contributes with insights on tumor immune escape, and help in tumor drug targeting. Tools to investigate such spatial omics approaches suffer from high costs and limited direct clinical applicability. We address this issue by proposing a Machine Learning model which learns to
