carlo.dedonno's repositories
CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
cdedonno
Github homepage
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comp-neuro
Code and notebooks for Computational Neuroscience class at TUM
Language:Jupyter Notebook000
gitignore
A collection of useful .gitignore templates
CC0-1.0000
lish-moa
Kaggle competition MoA prediction
Language:Jupyter Notebook000
scgen
Single cell perturbation prediction
Language:PythonGPL-3.0000
Variational-Autoencoder-PyTorch
Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset
Language:Python000