Predicting Cell Health with Morphological Profiles
-
Updated
Jan 26, 2022 - HTML
Predicting Cell Health with Morphological Profiles
Processed Cell Painting Data for the LINCS Drug Repurposing Project
Predicting pharmacodynamic responses to cancer drugs using cell morphology
👩🍳 Recipe repository for image-based profiling of Pooled Cell Painting experiments
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.
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
[CVPRW 2024] Learning interpretable single-cell morphological profiles from 3D Cell Painting z-stacks
Accompanying code for Image2Omics
Single cell analysis of the JUMP Cell Painting consortium pilot data (cpg0000)
Image-based profiling and machine learning to predict failing vs. non-failing cardiac fibroblasts
🛠️ Use me to version control Pooled Cell Painting data and processing pipelines
quickly generate overviews of Cell Painting image plates
Add a description, image, and links to the cell-painting topic page so that developers can more easily learn about it.
To associate your repository with the cell-painting topic, visit your repo's landing page and select "manage topics."