Yun Ge

LagCI identifies temporal causal relationships in dense multi-omics time series by analyzing lag-correlation profiles and filtering spurious associations.

TidyMass2 improves untargeted metabolomics by inferring metabolite origins and analyzing functional modules, increasing biologically interpretable features from 5.8% to 58.8% in pregnancy urine data.

MAPA uses large language models to reduce redundancy in pathway analysis and improve biological interpretation of multi-omics data.