Jiaqi Zhang

The paper presents a machine learning challenge to predict gene perturbation effects on T cell differentiation, finding that feature representation from perturbational data improves performance and validating top genes experimentally.

This paper introduces SCCVAE, a hybrid model combining mechanistic causal models with deep learning to predict and simulate genetic perturbation responses in single-cell data.

MORPH predicts cellular responses to genetic perturbations across data modalities and conditions using a variational autoencoder and attention mechanism.