Xushen Xiong

TopicVI identifies context-specific gene programs in single-cell and spatial transcriptomics data by integrating biological knowledge with data-driven refinement.

The paper develops BayesMonSTR to detect mosaic STR mutations at single-cell resolution and finds increased STR indels in aging cells, particularly in prefrontal cortex neurons.

This study identifies a polyclonal-to-monoclonal evolutionary trajectory in lung squamous cell carcinoma using genomic and transcriptomic data, with implications for precision therapeutics.

The paper identifies the Z-RNA-ZBP1 axis as a driver of Tau-mediated neurodegeneration and shows that ZBP1 inhibition can alleviate cognitive deficits in Tau-transgenic mice.

This study shows that ADAT2/3-mediated tRNA editing supports cancer cell growth and tumorigenicity by enhancing translation of growth-promoting mRNAs.

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

This study integrates genotype, epigenome, and phenotype data to show immune cells contribute to bipolar disorder pathogenesis, heterogeneity, and therapy through epigenomic and genetic mechanisms.

The paper introduces TransRBP, a deep learning framework that models RBP binding and m6A interactions, revealing variant-RBP combinations linked to genetic diseases.

The paper introduces Translatomer, a transformer model that predicts cell-type-specific translation from mRNA and sequence data, identifies disease variants affecting translation, and reveals non-eQTL regulatory mechanisms.

The paper profiles m6A and m6Am methylomes across 42 human and 16 mouse tissues, revealing tissue-specific patterns and cross-species conservation.