Shuang Yang

The paper introduces GDAS, a high-throughput glycoproteomics platform that identifies disease-specific glycoforms using mass spectrometry data and machine learning.

This paper identifies and predicts O-glycosylated RNA using a chemoenzymatic method and a computational tool, revealing their role in pancreatic cancer progression.

The paper develops and optimizes methods to enrich and identify glycoRNAs, demonstrating efficient digestion and preservation techniques for studying their role in immune responses and disease.

The paper identifies increased fucosylation patterns in saliva glycoproteins of lung cancer patients, suggesting potential for early diagnosis of lung adenocarcinoma.