Hiroyuki Yamamoto

OAC-PCA improves PCA for metabolomics by ensuring orthogonal components and correlation-based loadings, enabling better statistical analysis of metabolite associations with confounding factors.

This study uses multiomics to analyze the anti-inflammatory effects of Glycyrrhiza uralensis and isoliquiritigenin in macrophages, identifying metabolic and phosphorylation changes associated with inflammation suppression.

This study evaluates DIA techniques for CE-MS/MS metabolomics, showing vDIA with MS-DIAL improves annotation rates and accuracy, and identifies new metabolites in LPS-treated macrophages.

This paper introduces multiset PLS-ROG for integrating multi-omics data, clarifies its statistical properties, and provides an R package for implementation.