Genomics England uses Amazon SageMaker to predict cancer subtypes and patient survival from multi-modal data

In this post, we detail our collaboration in creating two proof of concept (PoC) exercises around multi-modal machine learning for survival analysis and cancer sub-typing, using genomic (gene expression, mutation and copy number variant data) and imaging (histopathology slides) data. We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker. These multi-modal pipelines are being used on the Genomics England cancer cohort to enhance our understanding of cancer biomarkers and biology.