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: A neural network framework that predicts transcriptional responses to both single- and two-gene perturbations.
: A machine learning architecture designed to predict cellular responses to perturbations across diverse biological contexts. perturbation
: A model that extends perturbation studies from static snapshots to dynamic cellular trajectories, allowing for the simulation of disease progression or development. : A neural network framework that predicts transcriptional
: A causally inspired graph neural network that identifies which combinations of perturbations are needed to reverse a disease phenotype. Software & Frameworks : A causally inspired graph neural network that
: A meta-learning framework that translates existing perturbation atlases to predict responses in new biological contexts using only a few "seed" perturbations.
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.
Silo + Milo
