High-grade serous carcinomas (HGSCs) account for approximately 70% of all epithelial ovarian cancers and patients presenting with these tumours have the lowest survival rates. Although HGSC tumours appear morphologically similar, they constitute different molecular subtypes. This presentation outlines my co-op project, which proposes a three-stage model development and validation approach to diagnose patients into these subtypes across gene expression platforms. These model development stages include subtype discovery, which is followed by model training and external validation using data on a similar and different gene expression platform. Finally, a chosen classifier will be used to assign HGSC molecular subtype classes to all available data using clinically applicable gene expression methods.