Zitong Jerry Wang

This paper introduces CIFM, a generative virtual tissue model that simulates transcriptional dynamics under therapeutic conditions to design immunotherapy strategies for cancer and autoimmune diseases.

The paper develops a deep-learning model to identify minimal tumour perturbations that enhance T-cell infiltration using spatial proteomic data from cancer samples.

This paper introduces SC-VAE, a supervised contrastive framework for analyzing cell perturbation data, showing improved sensitivity in detecting perturbation effects compared to existing methods.

The paper introduces a contrastive analysis framework to disentangle perturbation effects from natural cell variation in optical pooled screens, showing improved performance in generating embeddings and reducing technical artifacts.

The paper shows that optimizing receptor localization in cells enhances information acquisition in spatially structured environments, improving navigation efficiency by 30-fold through dynamic redistribution.