Cancer Research
Showing 7 of 7 projects
A vision transformer foundation model pre-trained on over 200 million pathology images for computational pathology tasks.
A Python package for 3D single-cell shape analysis using geometric deep learning on point clouds and voxels.
A natural language processing system that extracts and summarizes cancer phenotype information from clinical text documents.
A sequential model optimization platform that uses active learning to discover synergistic drug combinations for cancer treatment with minimal screening.
PyTorch implementation of twin graph neural networks with similarity augmentation for drug response prediction using protein-protein associations.
A deep learning model that predicts drug response by fusing multi-omics data with graph convolutional networks.
Attention-based GNN model for predicting drug sensitivity and explaining gene contributions using a drug-cell-gene heterogeneous network.
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