William Dee

SHOT-CCR improves classification of genetic perturbations in Cell Painting datasets by reducing batch effects through biologically guided adversarial training.

The paper shows that cell counting can predict compound bioactivity as well as Cell Painting or gene expression, but Cell Painting adds unique value in protein-target-specific benchmarks.

This paper uses GANs to generate synthetic thyroid histopathology images, improving model generalizability and performance on minority classes.

This study benchmarks zero-shot single-cell foundation model embeddings for perturbation effect prediction and finds they do not consistently improve over baseline models under distribution shift.

This paper introduces LMPred, a model using pre-trained language models and convolutional neural networks to predict antimicrobial peptides with high accuracy.