Ju‐Sheng Zheng

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 paper identifies batch effects and primer mismatches in the Human Microbiome Project's ITS2 sequencing data.

This study identifies food pairing patterns linked to cardiometabolic traits across populations, showing their independent association beyond single-food effects and dietary indices.

This study introduces METRIC, a deep-learning method that uses gut microbiome data to correct random errors in self-reported dietary assessments, showing effectiveness in simulated and real-world datasets.

metaExpertPro constructs metaproteomics spectral libraries and analyzes DIA-MS data, achieving high peptide quantification and low FDR in human fecal samples.