Martina Occhetta

The paper predicts cancer treatment response using a MIL-attention framework that analyzes macrophage states from single-cell RNA data, showing improved performance over existing methods.

MORGaN is a self-supervised framework for drug target discovery that outperforms existing models by leveraging multi-omic, multi-relation gene networks and provides interpretable results.

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.