Lihang Ye

SMECT benchmarks post-GWAS methods for spatial mapping of cells associated with human complex traits, revealing trade-offs between sensitivity and specificity across methods.

scPhoenix enhances sparse cellular multi-omics translation using contrastive learning and aux-core feature disentanglement, preserving heterogeneity and improving performance in high sparsity and spatial heterogeneity scenarios.

This paper introduces ELAG, an ensemble learning framework for polygenic risk prediction that improves accuracy in high-dimensional genomics data by using policy gradient methods to guide variant selection.

This study links the developmental expression of risk genes to the age of onset of neuropsychiatric disorders, showing that disease peaks align with gene expression peaks in the prefrontal cortex.