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](https://gevaertlab.github.io) · [Publications](https://gevaertlab.github.io/publications.html) · [GitHub](https://github.com/gevaertlab) · [Stanford profile](https://profiles.stanford.edu/olivier-gevaert)

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## 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).