Jiada James Zhan

MSMICA improves metabolite identification in untargeted metabolomics by integrating mass spectrometry data, retention time, and biological evidence, achieving high accuracy across mammalian datasets.

The paper detects enzymology-defined precursor-product correlations in human plasma LC-MS data, showing consistency across longitudinal and cross-sectional studies.

The paper develops and tests the Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolomics data, showing correlations with inflammation biomarkers.

The paper develops and validates dietaryindex, an R package for standardized dietary pattern analysis, showing full accuracy in calculations compared to manual and existing methods.