Yichen Henry Liu

CNVeil infers haplotype-specific copy number and subclonal architecture from single-cell cancer genomes, outperforming existing methods in accuracy and resolution.

This study benchmarks 20 cell type annotation methods on spatial transcriptomics data to evaluate their performance in resolving cell types, subtypes, and biological organization across diverse datasets.

This study evaluates long single-end barcoded reads for structural variant detection, showing improved accuracy with longer read lengths compared to short-read and pangenome-based methods.

MaskGraphene integrates multi-slice, multi-condition spatial transcriptomics data using a graph neural network to generate interpretable joint embeddings with preserved geometric information, improving downstream analyses.

This study trains RNNs on cognitive tasks to model prefrontal neuron maturation, finding increased delay period activity and activation in antisaccade tasks.

Aquila_stLFR is a tool for diploid genome assembly and structural variant calling using stLFR linked-read data, achieving high sensitivity for deletions and specificity for insertions.