Xiaodong Teng

This paper presents a computational strategy to optimize spectral library size for DIA-MS data, improving peptide and protein identification rates in colorectal and carcinoma samples.

The paper introduces DPHL, a pan-human protein mass spectrometry library built using DIA and PRM, and demonstrates its utility in identifying differentially expressed proteins in prostate cancer and DLBCL.

This paper develops a deep learning method using DIA tensor for phenotype prediction from mass spectrometry data, achieving high accuracy in cancer classification.