Wenkai Han

Unify: Learning Cellular Evolution with Universal Multimodal Embeddings

Unify learns universal cell embeddings across species by integrating RNA expression with protein and general language model embeddings, enabling cross-species comparisons and accurate perturbation response prediction.

Sensitive Transcriptomics and Genotyping reveals function of genetic variants in immunity

This study introduces STAG-seq, a method combining transcriptomics and genotyping to functionally analyze genetic variants in immune cells, revealing their impact on gene expression and disease mechanisms.

PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients' privacy in omic data

PPML-Omics is a privacy-preserving federated learning method that balances privacy protection and model utility across three omic data tasks.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

CLEAR is a self-supervised contrastive learning framework for scRNA-seq that improves clustering, batch correction, and pseudo-time inference by reducing technical noise and heterogeneity.

DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

DeeReCT-TSS improves TSS annotation across cell types using DNA sequences and RNA-seq data, outperforming sequence-only methods and enabling cell-type-specific TSS identification.