Xiran Liu

ACE-OF-Clust improves single-cell clustering by aligning results across methods and identifying informative features in scRNA-seq and spatial transcriptomics data.

ML-MAGES uses machine learning to improve genetic association analysis by shrinking inflated effect sizes and clustering variant associations across multiple traits.

The paper mathematically analyzes bounds on allele-sharing dissimilarity (ASD) statistics, showing how expected ASD values are constrained by allele frequencies, explaining narrow empirical ranges observed in human populations.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

This paper introduces a novel Diffusion Histology Imaging (DHI) approach combining DBSI and machine learning to improve detection and classification of glioblastoma histopathology features.