Yuehua Cui

CASTLE improves spatial domain detection by integrating cell-type information and contrastive learning, achieving better accuracy across tissues and resolutions.

SpatialESD improves spatial domain detection in spatial transcriptomics by integrating multiple methods and capturing both direct and indirect relationships between clusters.

This paper improves polygenic score prediction for underrepresented groups using transfer learning methods implemented in the GPTL R-package, showing better performance than traditional approaches.

SPACE improves spatial domain detection by clustering spatially variable genes while adjusting for cell type effects without requiring prior knowledge of clusters or cell types.

TESSA detects trajectory-preserved and spatially-variable genes in spatial transcriptomics, improving tumor progression analysis and spatial domain detection.

gnSPADE is a reference-free spatial deconvolution method that improves accuracy and resolution by incorporating gene network structures, applied to both synthetic and real spatial transcriptomics data.

MR-SPLIT reduces bias in one-sample Mendelian randomization by addressing weak instruments and selection bias through sample-splitting and cross-fitting.

BayesKAT is a Bayesian framework for genetic association studies that identifies joint genetic effects in complex diseases by adaptively selecting optimal kernels.

This study conducts a genome-wide association study (GWAS) to identify genetic factors associated with hypersensitivity pneumonitis, finding significant pathways but no individual SNPs or genes after multiple testing correction.

The paper introduces an efficient kernel-based method for gene set association analysis that controls type I error and achieves high power in high-dimensional settings.