Liang Qiao

This study introduces DeepGPO, a deep learning framework that accurately predicts N- and O-glycopeptide MS/MS spectra, enhancing glycoproteomics analysis.

The paper uses ZenoTOF mass spectrometry to analyze glycoproteomes from breast cancer and control samples, identifying 15 site-specific glycans as potential biomarkers for breast cancer detection.

DeepGP predicts glycopeptide MS/MS spectra and retention time using a hybrid deep learning framework, improving glycoproteomics analysis by distinguishing similar glycoforms and increasing detection sensitivity.

The paper introduces ConDiGA, a pipeline for constructing accurate protein sequence databases from metagenomic data to improve metaproteomics analysis, achieving deep coverage in synthetic and real microbiota samples.

The paper uses single-cell time-resolved multi-omics to reveal apoptotic and ferroptotic heterogeneity in macrophage-derived foam cells during atherosclerosis progression.

GproDIA improves glycoproteomics by enabling accurate glycopeptide identification and quantification using DIA data with comprehensive statistical control.