cdedonno

cdedonno

Github homepage

comp-neuro

Code and notebooks for Computational Neuroscience class at TUM

ConvAge

Convolutional Neural Network for prediction of future appearances on human faces.

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.

deep-face-saliency

gitignore

A collection of useful .gitignore templates

lish-moa

Kaggle competition MoA prediction

scgen

Single cell perturbation prediction

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.

scarches

Reference mapping for single-cell genomics

cdedonno/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/cdedonno

Github homepage

cdedonno/comp-neuro

Code and notebooks for Computational Neuroscience class at TUM

cdedonno/ConvAge

Convolutional Neural Network for prediction of future appearances on human faces.

cdedonno/deep-face-saliency

cdedonno/gitignore

A collection of useful .gitignore templates

cdedonno/lish-moa

Kaggle competition MoA prediction

cdedonno/scgen

Single cell perturbation prediction

cdedonno/Variational-Autoencoder-PyTorch

Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset