Bo Xia

The paper identifies ZNF654 and JMJD6 as novel factors defining TAD boundaries through in silico screens and experimental validation.

Chromnitron predicts protein-DNA binding landscapes across cell types and proteins using multimodal learning, revealing new regulators of T cell exhaustion and dynamic shifts during neurogenesis.

This study uses scRNA-seq to analyze transcriptional changes in human macrophages and Encephalitozoon intestinalis parasites during infection, revealing parasite developmental dynamics and host response.

The paper introduces a segmentation-free vision transformer, CANVAS, to discover and characterize tumor microenvironment signatures from high-dimensional multiplexed images, identifying a monocytic signature linked to poor prognosis.

The paper analyzes multi-omic data from two pig heart xenografts in human decedents to identify immune and metabolic responses associated with rejection and injury.

This study introduces C.Origami, a deep learning model that predicts cell type-specific 3D chromatin organization, enabling in silico genetic screening to identify genomic regions affecting chromatin structure.