Zeyu Lu

BOSE estimates sample mean and standard deviation from median, range, and interquartile range using Bayesian order statistics, showing robustness and improved performance in small samples and non-normal distributions.

G-SPRI predicts cancer driver events from missense mutations by integrating structural and mutation-specific features using a graph model.

The paper develops eTRex, a variational Bayesian model, to analyze 4,819 ATAC-seq datasets and create a pan-cancer atlas of transcriptional regulator profiles, revealing common and context-specific regulators.

MisTIC corrects transcript misassignment in spatial transcriptomics data, improving cell type identification, differential expression analysis, and RNA localization.

BIT identifies transcriptional regulators using a Bayesian hierarchical model, improving accuracy and interpretability by integrating epigenomic data with TR ChIP-seq libraries.

This paper reviews and benchmarks NGS-based computational methods for predicting transcriptional regulators using query gene sets, finding that BART, ChIP-Atlas, and Lisa perform best.

Cytomulate simulates CyTOF data accurately and efficiently, outperforming existing methods in capturing data characteristics and learning distributions.