Michael Q. Zhang

SpatialZ generates dense 3D cell atlases from planar spatial transcriptomics data by reconstructing virtual slices, validated with real 3D data and enabling new spatial analyses.

The paper introduces ChIPr, a model predicting cohesin-mediated chromatin interactions from 2D features, showing strong correlation with ChIA-PET data and capturing cell-type-dependent loops.

The paper shows that ecDNA interactomes are shaped by nascent RNA, with PVT1 and MYC ecDNA using noncoding RNA for trans-activation, while EGFR ecDNA is less effective due to efficient splicing.

This paper introduces a data-driven method to learn a jump diffusion process from aggregate gene expression data, enabling prediction of gene expression dynamics and RNA velocity.

This study quantifies how microRNAs modulate gene expression noise and shows how this affects synthetic gene circuits and cell type classification.

MarkovHC is a clustering algorithm that identifies cell types, trajectories, and critical points in single-cell omics data by reconstructing a pseudo-energy landscape based on metastability theory.

The paper shows MyoD organizes 3D genome architecture in muscle cells and demonstrates H3K27ac is insufficient for MyoD-induced chromatin loops.

The paper identifies 3D genome alterations and enhancer hijacking mechanisms in T-lineage acute lymphoblastic leukemia (T-ALL) using integrated analyses of 18 patients and 4 controls.

The paper shows that transcriptional responses to thermal stress in human cells are prewired in the 3D genome, with chromatin and epigenetic settings playing a key role.

This paper introduces GEM-FISH, a method that integrates Hi-C and FISH data to improve 3D chromosome modeling, showing better accuracy in TADs, compartments, and super-enhancer distributions than Hi-C alone.

SuperCT improves single-cell transcriptomic analysis by using a supervised classifier to accurately identify cell types from RNA expression profiles, outperforming traditional unsupervised methods.

This study introduces a competition motif model to analyze molecular competition's role in gene regulatory networks, revealing its impact on dose-response curves, response speed, noise, and target correlations.

The paper compares human and mouse dorsal root ganglia transcriptomes using RNA-seq, identifying enriched genes and pathways relevant to pain and sensory neuroscience.

The paper identifies recurrently deregulated lncRNAs in HCC and PVTT samples, linking them to metastasis and prognosis using multi-omics data.