Yunping Zhu

The paper introduces π-MSNet, a large-scale proteomics dataset for AI training and benchmarking, showing performance improvements when models are retrained on it.

PTMExplorer is a visualization platform that integrates PTM data with 3D structure, functional annotations, and disease associations to identify biologically significant modification sites.

DeepRTAlign improves retention time alignment for large cohort LC-MS data, enhancing identification sensitivity without compromising quantitative accuracy and enabling predictive modeling for hepatocellular carcinoma recurrence.

Pathformer integrates multi-omics data using a pathway-informed Transformer to improve cancer diagnosis and prognosis prediction.

The paper introduces the Universal Spectrum Identifier (USI) to standardize referencing mass spectra in proteomics repositories, enabling transparent traceability of spectral evidence in publications.

The paper introduces AP3, an algorithm that improves proteotypic peptide prediction by incorporating peptide digestion probability, achieving higher accuracy than existing tools.

LFAQ improves label-free absolute protein quantification by correcting MS intensity biases using predicted peptide quantitative factors, achieving higher accuracy and precision, especially for low-abundance proteins.

PANDA is a proteomics data quantification tool that offers higher accuracy and precision than MaxQuant with reduced computation time, supporting both label-free and labeled quantification.