Bo Wang

OCAT integrates large-scale scRNA-seq datasets efficiently and achieves state-of-the-art performance in cell type clustering and downstream analyses.

simATAC generates synthetic scATAC-seq datasets with known cell labels to benchmark analytical pipelines.

Network Enhancement improves the signal-to-noise ratio of weighted biological networks, enhancing downstream analysis performance across multiple applications.

SIMLR is a tool for single-cell RNA-seq analysis that improves clustering and visualization through multi-kernel learning.

This paper introduces SIMLR, a similarity-learning framework for single-cell RNA-seq data analysis, showing improved clustering and visualization.

Reveel improves low-coverage whole-genome variant calling and genotyping by leveraging linkage disequilibrium, achieving higher accuracy and lower computation cost than existing methods.