Subtype-DIVIDE: A Self-supervised Diffusion Contrastive Framework for Multi-omic Cancer Subtyping

Subtype-DIVIDE is a self-supervised diffusion contrastive learning framework for multi-omic cancer subtyping. The capsule provides code to preprocess multi-omic data, train the Subtype-DIVIDE model, and evaluate discovered cancer subtypes using downstream clustering and survival/clinical analyses. This compute capsule is intended to support reproducibility of the experiments described in the associated manuscript. It includes the main training/inference script and helper utilities, along with instructions to run the pipeline on supported datasets.