Yi Zhu

The paper presents a comprehensive spatial proteome map of human tissues and cancers, enabling drug response analysis and target discovery.

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.

The paper develops a foundation model using perturbation proteomics data to predict drug responses and improve breast cancer prognosis.

Integral-Oms enables sequential extraction and profiling of multiple omics layers from biopsy tissue, revealing suppressed ferroptosis in colorectal cancer tissues.

This study identifies a 24-protein panel distinguishing TNBC from other breast cancers and shows proteomics improves drug sensitivity predictions.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

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 study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

This study uses PCT-SWATH to analyze proteomic changes in HCC biopsy samples, identifying 23 upregulated pathways and 13 downregulated ones, with notable changes in virus-infection-related proteins in HBV+ patients.