Yingying Sun

This study uses population-based metaproteomics to identify functional associations between gut microbiota and 39 phenotypes, including 1,381 features related to Type 2 Diabetes.

This study evaluates blood contamination in nanoparticle-based plasma proteomics, identifies contamination sources, and introduces software to detect and mitigate contamination in clinical samples.

DIA-BERT improves DIA proteomics analysis by using pre-trained transformer models, achieving higher protein and peptide identifications than DIA-NN in cancer samples.

This paper shows that the TONSL-MMS22L complex and FANCM form an interdependent chromatin complex to resolve replication stress and improve cancer prognosis.

DDA-BERT improves PSM rescoring in proteomics using a transformer-based model trained on 95 million human spectra, outperforming existing methods across species and sample types.

MetaDIA reduces the search space for DIA gut metaproteomics by prioritizing peptides based on species and functional abundance, achieving results consistent with traditional DDA-based methods.