Yingwei Hu

This study identifies pig xenoantigens reactive to human preformed antibodies using proteomic and glycoproteomic methods, revealing potential targets for improving xenotransplantation.

This study develops a high-sensitivity spatial proteomics method, SP-Max, to analyze protein expression changes in pancreatic tissues from normal to cancerous states.

The paper uses proteomics data and machine learning to identify protein signatures distinguishing renal cell carcinomas from normal tissues and different subtypes.

The paper introduces GPnotebook, a glycoproteomic database and toolkit analyzing cancer-associated glycosylation changes using data from CPTAC.

OmicsOne simplifies multi-omics data analysis by integrating tools for statistical analysis, machine learning, and visualization to associate molecules or pathways with phenotypes.

The paper introduces EXoO, a method for site-specific extraction of O-linked glycopeptides, enabling the mapping of over 3,000 O-linked glycosylation sites across human tissues and revealing significant differences between tumor and normal kidney tissues.

The paper reanalyzes proteomic and phosphoproteomic data to identify a large number of previously unknown N-linked glycopeptides using GPQuest 2.0.