Jingtian Zhou

Bolero predicts cell-state-specific gene regulation from DNA across species by integrating cell-state embeddings and biological variables.

This paper introduces Spatial Hi-C-RNA, a spatial multi-omics technology that co-maps chromatin conformation and transcriptome in tissue, revealing spatially coherent domains and new insights into gene regulation.

The paper maps 3D genome organization and DNA methylation across 86,689 single nuclei from 16 human tissues, revealing cell-type-specific variations and discrepancies between methylation and chromatin structure.

This study examines age-related changes in the human hippocampus using multi-omics data, revealing cell-type loss and epigenetic reprogramming linked to cognitive decline.

This study identifies how genetic and environmental factors shape the human immune cell epigenome through differential methylation and chromatin accessibility changes.

An LLM agent with a literature-mined environment of 150 tools, 105 packages and 59 databases plans in code, matching reported human accuracy on database and sequence question answering.

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

KGWAS improves GWAS power in small cohorts by integrating a functional knowledge graph, discovering more associations for rare diseases.

This paper introduces scPRS, a deep learning model that deconvolves polygenic risk scores at the single-cell level to uncover cellular and molecular disease mechanisms in complex human diseases.

The paper generates a single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain, identifying methylation patterns and regulatory networks across cell types and brain regions.

This study identifies AD-specific DNA methylation signatures and chromosomal epigenome erosion in LOAD brain cells and shows their conservation in induced neurons.

scMD estimates cell type fractions from bulk DNAm data using single-cell DNA methylation references, showing improved performance in identifying Alzheimer's disease-related methylation patterns.

The paper links single-cell epigenomes and projection patterns of 33,034 mouse neurons to identify transcriptomic and epigenomic properties of projection cell types.

The paper analyzes gene regulatory programs in the neocortex of rodents and primates using single-cell multiomics, finding rapid evolution of cell type-specific gene expression and TE-driven cCREs.

The paper identifies 188 cell types in the human brain using single-cell DNA methylation and 3D genome data, and develops a tool to predict cell types based on methylation patterns.

The paper investigates epigenomic and 3D chromatin conformation changes in human frontal cortex and hippocampus development using sn-m3C-seq, finding distinct chromatin conformations in neurons and glial cells and linking risk variants to regulatory regions.

The paper develops a multi-omic single-nucleus sequencing method and framework to analyze regulatory genome diversity in human cortical cells and link it to disease risk variants.

The paper generates a multimodal cell census and atlas of the mammalian primary motor cortex, revealing a unified molecular genetic landscape and cross-species taxonomy of cell types.

The paper constructs a DNA methylation atlas of the mouse brain at single-cell resolution, revealing epigenetic diversity and spatial organization of neuronal subtypes.

The paper links epigenomic properties of cortical projection neurons to their anatomical and projection patterns using single-nucleus DNA methylation sequencing in mice.

HiCluster improves clustering of low coverage single-cell Hi-C data by imputing using linear convolution and random walk, revealing TAD-like structures consistent with bulk data.

The paper develops sn-m3C-seq to profile chromatin conformation and DNA methylation in single nuclei, enabling high-resolution cell-type classification and chromatin map reconstruction in mice.

This paper introduces snmC-seq2, an improved single-cell DNA methylome profiling method that enhances read mapping, reduces artifacts, and increases throughput and coverage uniformity.

This paper introduces DTINet, a network integration method for predicting drug-target interactions and repositioning drugs, showing improved performance and validated novel COX inhibitor applications.

ROSE predicts ribosome stalling from ribosome profiling data, showing better performance than conventional models and revealing regulatory factors and functional interactions.