Chunlei Liu

Hydra is a deep generative framework for single-cell omics data that improves cell type annotation and feature selection, particularly for rare populations.

This study benchmarks single-cell multimodal omics integration methods across multiple tasks to guide method selection based on data characteristics and study goals.

The paper develops SnapKin, a deep learning ensemble model for kinase-substrate prediction from phosphoproteomics data, achieving the best performance in this task.

SnapCCESS improves clustering of multimodal single-cell omics data by integrating multiple data modalities using an ensemble deep learning framework.

Matilda is a multi-task learning method that integrates single-cell multimodal omics data by performing data simulation, dimension reduction, cell type classification, and feature selection.