Yiqing Wang

This paper introduces a supervised contrastive learning framework for multi-omics prediction models, showing improved accuracy over single-omics approaches in type 2 diabetes and high-fat diet studies.

The paper identifies how protein stability, turnover, and abundance influence the connectivity of protein-protein interaction networks in yeast.

ANYI integrates yeast PPI data with sixteen biophysical and regulatory annotations to enable cross-layer interactome analysis.

The paper identifies common gene regulatory signatures across multiple inflammatory skin diseases using an integrative systems biology framework and proposes potential drug targets.

MB-SupCon integrates microbiome and metabolomics data to improve prediction accuracy in microbiome-only datasets, achieving high accuracy in predicting insulin resistance, sex, and race.

MetaPrism enables joint analysis of taxa and gene abundances in metagenomic data to identify potential biomarkers for therapeutic response prediction.

The paper identifies circRNAs in porcine oocytes and cumulus cells, showing circARMC4 is essential for oocyte maturation and early embryo development.