James Urban

This paper introduces a framework for automated glycan nomenclature conversion that accepts any input format and outputs a standardized IUPAC-condensed format, enabling seamless integration of glycan data.

This paper develops a machine learning workflow to identify generalizable diagnostic ions for O-glycan isomer differentiation using a curated dataset of 237,000 spectra.

The paper introduces CandyCrunch, a deep learning model that predicts glycan structures from tandem mass spectrometry data with high accuracy, enabling high-throughput glycomics.

This paper introduces a computational framework for differential glycomics expression analysis, demonstrating improved robustness and reliability through specialized methods and an open-source tool.

GlycoDraw generates high-quality, SNFG-compliant glycan figures in Python, enabling automated annotation and integration into glycan analysis workflows.