AI and the World's Hard Problems · Life sciences · Nature (machine learning) · 2026-10-07
Generalizable perturbation prediction
Cell perturbation data reveal causal regulatory relationships, but such data are often scarce, which motivates methods that predict responses in one context from others. The authors developed State, a machine-learning model that combines a transformer-based state transition model for single-cell perturbation effects with a pretrained cell-embedding model. It was trained on perturbation data from over 100 million cells and observational data from 167 million cells, and the team also built Cell-Eval, an evaluation framework for comparing models.