Peng Gao

ProMeta improves disease diagnosis and prediction from plasma proteomics using a meta-learning framework that overcomes data scarcity with biological priors and interpretable features.

MetaNet is an R package for scalable multi-omics network analysis, offering faster computation and better memory efficiency than existing tools, with applications in microbial and exposome-transcriptome studies.

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.

Prophet is a transformer-based framework that improves disease diagnosis and prediction using proteomics data from the UK Biobank, achieving high accuracy and identifying new protein-disease associations.

tidyMass provides a reproducible and shareable framework for LC-MS metabolomics data analysis.

This study combines common and rare variant analyses with single-cell multiomics to identify cell-type-specific genetic risk factors for severe COVID-19, revealing molecular mechanisms and potential therapeutic targets.

This study integrates longitudinal personal exposome and multi-omics data to show dynamic interactions between environmental exposures and internal biomolecules, highlighting key agrochemicals and fungi.