Daniel Wong

The paper uses single-cell foundation models to identify genetic targets that reverse inflammation in endothelial cells by analyzing Perturb-seq data with and without proinflammatory stimulation.

The paper introduces a simple baseline method that outperforms deep learning models in predicting genetic perturbations and highlights the effectiveness of foundation models with fine-tuning.

The paper introduces MOAProfiler, a deep learning encoder that improves the accuracy of drug mechanism of action classification from Cell Painting images compared to traditional and existing deep learning methods.

The paper introduces a microfluidic system for single-cell transcriptome profiling and demonstrates its ability to identify cell cycle regulators.