NOTE: this code will be deprecated soon. The new version, Latent Event Mapping (LEMING), will be found on the lab website here: https://github.com/lililab-sussex/leming/
The vEBM is a probabilistic disease progression model that leverages optimal transport to scale to large feature sets, enabling rapid, low-compute inference of fine-grained multi-modal trajectories. It can also use any combination of multi-modal features, not just neuroimaging, e.g., clinical test scores, biofluids, genomics.
If you use the vEBM, please cite this paper:
Wijeratne, PA & Alexander, DC (2024). "Unscrambling disease progression at scale: fast inference of event permutations with optimal transport". Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.2410.14388
Install directly from GitHub using pip:
pip install git+https://github.com/pawij/birkhoff.git| Package | Version |
|---|---|
| numpy | >=1.26, <2 |
| scipy | >=1.11 |
| scikit-learn | >=1.3 |
| torch | >=2.2 |
| matplotlib | >=3.7 |
Python 3.10 or higher is required.
Here we apply the vEBM to structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It shows pixel-level disease progression events in the brain, providing new fine-grained insights into changes at the tissue-level caused by Alzheimer's disease.
Training this model took only 5 minutes on a single laptop CPU.
- Peter Wijeratne (p.wijeratne@pm.me)
- Misha Jairamani