Qing Hua Nie

The paper develops a mathematical model to explain the formation and function of bivalent chromatin by analyzing the dynamics of H3K4me3 and H3K27me3 modifications.

This paper introduces scFAN, a deep learning model that predicts genome-wide TF binding in single cells using single-cell ATAC-seq data and pre-trained on bulk ATAC-seq, DNA sequence, and ChIP-seq data.

This study identifies age-specific gene signatures in the human cerebral cortex using TCA, linking them to structural features and neuropsychiatric disorders.

The paper uses single-cell RNA sequencing to identify spatially distinct basal stem cell populations in human epidermis and shows their role in maintaining epidermal homeostasis through signaling and differentiation.

The paper investigates how Dpp morphogen gradients scale in Drosophila wing discs and proposes an alternative feedback model to explain scaling failure in mutants.

The paper models EMT-immune dynamics in epithelial cancer progression, identifying key factors that influence invasion-free survival and predicting new genes affecting cancer outcomes.

The paper uses single-cell transcriptomics to define basal cell states in skin homeostasis and wound healing, identifying distinct transcriptional states and their dynamics.

This paper uses mathematical modeling to show that spatially uniform nonlocal feedback mechanisms can enhance robustness in signaling gradients, offering a new theoretical approach for promoting stable signaling.

This paper introduces SoptSC, a method that integrates clustering, lineage inference, and communication network prediction in single-cell transcriptomics using a similarity matrix optimization framework.

Topographer identifies cell fate mechanisms by analyzing single-cell transcriptomic data, revealing dynamic gene expression patterns and cell-state transitions during myoblast differentiation.

SOMSC uses a self-organizing map to identify cellular states and their transitions in high-dimensional single-cell gene expression data.