Donghai Liang

This paper introduces h-ARF, a high-dimensional extension of adversarial random forests for generating synthetic omics data, showing improved performance in preserving feature distributions and downstream tasks.

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 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.