PeijingZhang/Shennong
A deep-learning framework to predict the tumor-associated cells' reaction to pharmacologic perturbations at the single-cell level.
This framework helps cancer researchers and pharmacologists screen anticancer drugs by predicting how tumor cells react to different compounds at a single-cell level. It takes single-cell RNA sequencing (scRNA-seq) data and drug perturbation signatures to predict individual cell responses, potential tissue damage, and drug mechanisms. Researchers can use this to accelerate drug discovery and enhance drug screening accuracy.
No commits in the last 6 months.
Use this if you need to virtually screen anticancer drugs, understand single-cell responses to perturbations, and evaluate potential drug candidates without extensive wet-lab experimentation.
Not ideal if you require direct experimental validation or need a tool for broad-spectrum drug discovery outside of oncology.
Stars
8
Forks
1
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Sep 15, 2025
Commits (30d)
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