To achieve this, we developed a machine learning framework called BioDisCVR for discovering data-driven ratio-based biomarkers driven by a clinical objective.19 The framework is general (e.g. could be used on fluid biomarkers, or other features such as regional volumes, cortical thickness, cognitive scores) but here, we apply it to tau PET imaging data for clinical trials on Alzheimer’s disease. This evidence concerns the gene MAPT and Alzheimer disease.