Fang Wang

This paper introduces scTranslator, a pre-trained large generative model that translates single-cell transcriptomes to proteomes, showing accuracy and utility in multiple downstream tasks.

The paper introduces tFold-TCR, a model for predicting TCR-pMHC structures, achieving higher accuracy and speed than existing methods and creating a large database of these structures.

This paper introduces scpDeconv, a deep learning method for deconvolving cell type mixtures in proteomic data, showing improved performance across species and technologies.

The paper compares transcriptomic responses in zebrafish following rod photoreceptor or bipolar cell loss, identifying cell-specific and shared regenerative gene programs and immune-related regulators.

The paper introduces scPROTEIN, a deep graph contrastive learning framework for single-cell proteomics that addresses uncertainty, denoising, batch effects, and embedding learning.

scBERT is a pre-trained deep learning model for cell type annotation in single-cell RNA-seq data, showing improved performance and robustness compared to existing methods.

This study identifies gene regulatory networks controlling proliferative and neurogenic competence in retinal Muller glia.