Zhiwei Wang

This paper introduces Harmonics, a framework for identifying common and condition-specific cell niches in spatial omics data, demonstrating its effectiveness across diverse datasets and diseases.

CellART extracts single-cell information from high-resolution spatial transcriptomics data across multiple platforms, demonstrating efficiency and accuracy in cancer datasets.

MRMU improves Mendelian randomization by accounting for measured covariates and unmeasured confounders, identifying robust risk factors for coronary artery disease and novel drug targets.

XMR improves causal inference in underrepresented populations by using cross-population GWAS summary statistics to increase instrumental variables and enhance statistical power.

This study benchmarks 15 Mendelian Randomization methods using real genetic data to evaluate their performance in causal inference under various confounding scenarios.

XMAP improves fine-mapping by using cross-population GWAS data and accounting for confounding bias, enhancing statistical power and computational efficiency.