: A model that extends perturbation studies from static snapshots to dynamic cellular trajectories, allowing for the simulation of disease progression or development.

Several recent papers and frameworks focus on predicting these responses using machine learning: Key Research Papers (2024–2026)

: A machine learning architecture designed to predict cellular responses to perturbations across diverse biological contexts.

: A meta-learning framework that translates existing perturbation atlases to predict responses in new biological contexts using only a few "seed" perturbations.

: A neural network framework that predicts transcriptional responses to both single- and two-gene perturbations.

: A causally inspired graph neural network that identifies which combinations of perturbations are needed to reverse a disease phenotype. Software & Frameworks

In the context of biology and machine learning, a "perturbation" typically refers to an experimental intervention—such as a genetic knockout or chemical treatment—that alters a cell's state to study its response.