Lijia Yu

GHIST+ reconstructs tissue-wide molecular maps from H&E histology, enabling cohort-scale molecular reconstruction from sparse spatial transcriptomic data.

This study evaluates how large language models generate summaries of bioinformatics analyses, finding they produce readable and safe summaries but often misinterpret visualizations and lack novel insights.

CLUEY improves cell type detection and clustering in single-cell omics by integrating biological knowledge to guide clustering and enhance interpretability.

This paper benchmarks single-cell methods by analyzing 282 papers, highlighting challenges in combining benchmarking studies and preventing fatigue.

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

This paper develops a non-invasive urine-based test for bladder cancer detection using driver gene mutations and aneuploidy analysis, showing high sensitivity and specificity compared to cytology.