Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
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Updated
Jan 24, 2022 - Jupyter Notebook
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
Predicting Cell Health with Morphological Profiles
Processed Cell Painting Data for the LINCS Drug Repurposing Project
quickly generate overviews of Cell Painting image plates
Benchmarking data processing strategies for Cell Painting data of NF1 Schwann cells. See analysis repository (https://github.com/WayScience/NF1_SchwannCell_data_analysis) for information on how the data was interpreted.
Accompanying code for Image2Omics
🛠️ Use me to version control Pooled Cell Painting data and processing pipelines
👩🍳 Recipe repository for image-based profiling of Pooled Cell Painting experiments
Predicting pharmacodynamic responses to cancer drugs using cell morphology
[CVPRW 2024] Learning interpretable single-cell morphological profiles from 3D Cell Painting z-stacks
Single cell analysis of the JUMP Cell Painting consortium pilot data (cpg0000)
Image-based analysis of cardiac fibroblast datasets to uncover proprietary drug impact on reversing fibrosis
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