Learning Genetic Perturbation Effects at Single-Cell Resolution for Virtual Cells
Jiaqi Zhang's talk focuses on advancing causal discovery and perturbational modeling for gene networks, crucial for therapeutic insights. She introduces theoretical guarantees for learning latent causal variables and presents MORPH, a VAE-based framework that predicts the effects of novel and combinatorial gene perturbations by integrating diverse prior biological knowledge and using distributional loss for training. The model has demonstrated success in guiding experimental design, including identifying novel T-cell regulators for cancer immunotherapy, highlighting its potential to accelerate biological discovery and drug development.
