Predicting Cell Health with Morphological Profiles
- carpenter-lab
- morphological-profiling
- crispr
- cell-painting
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Predicting Cell Health with Morphological Profiles
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
XitoSBML - the spatial SBML plugin for ImageJ
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning
DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
GNN-based gene-selection deep learning model for drug response prediction