README.md

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title: Gevaert Lab
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The Gevaert Lab develops machine-learning methods that integrate genomics, pathology, imaging, and clinical data — advancing precision medicine in oncology and cardiovascular disease, and building toward a medical digital twin that predicts disease trajectories and treatment response.

Stanford Medicine · Department of Medicine & Biomedical Data Science · Directed by Olivier Gevaert

Lab website · Publications · GitHub · Stanford profile

Research

We pursue the twin across the molecular-to-clinical stack, each area anchored by open methods:

• Molecular & epigenomic modeling — driver genes and methylation subtypes from multi-omics (MethylMix, AMARETTO).
• Computational pathology — gene expression from whole-slide images (SEQUOIA) and synthetic tissue generation.
• Quantitative imaging & radiogenomics — imaging phenotype linked to molecular state and outcome (LungNet).
• Integration & generative modeling — multimodal fusion and meta-learning (GeNNius); conditional 3D image synthesis (CONFLUX, BrainG3N).

Open resources — models, datasets, and demos are listed below (non-commercial research; see each repository for license and citation).