Advancing morphological profiling with self-supervised learning through comparative insights into representation learning of cellular perturbations nature.com

Researchers compared four self-supervised vision models trained on about 1.77 million Cell Painting images from 119,876 chemical perturbations in the JUMP–CP dataset. In tests of compound retrieval and mechanism-of-action identification, DINO and DINOv2 generally outperformed the classical CellProfiler method and masked-learning models, with up to 31–37% higher mean average precision on an independent dataset.