Yue Cheng Deng

This paper introduces an unsupervised image transformation method for optical microscopy that avoids data annotation and achieves high-fidelity image transformations across modalities.

This paper introduces MUSE, a method that combines single-cell morphology and transcriptional data to better characterize tissue heterogeneity and identify subpopulations missed by individual modalities.

This paper introduces a deep learning model to improve GWAS by detecting complex genetic effects, revealing potential causal pathways in disease development.

The paper introduces scScope, a deep learning method for cell-type composition identification from large-scale single-cell RNA-seq data.