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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.

License: MIT License

Python 0.21% Jupyter Notebook 99.78% Shell 0.01%

cpa's Introduction

Hi there 👋

  • 🔭 I’m currently working on machine learning methods for DNA and RNA sequence modelling.
  • 🌱 I’m currently learning how to make code production-worthy.
  • 💬 Ask me about sequence modelling, perturbation modelling, data integration, data analysis of -omics data.
  • 📫 How to reach me: [email protected].
  • 😄 Pronouns: he/him.
  • ⚡ Fun fact: I would prefer not to.

Tools 🛠️

Python  PyTorch  GCP  VSCode  Github  Ubuntu  Pandas  R 

Hobbies 🎮 🎸 📽 ⚽ 📜 🌊

My scrobbles

cpa's People

Contributors

m0hammadl avatar scottgigante-immunai avatar

Stargazers

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