Jun Ding

ProtSyntax is a protein language model that improves PTM site prediction and functional inference by integrating contextual and structural information.

The paper identifies sex-specific regulatory dynamics in adolescent brain development that may contribute to depression vulnerability through hormone-sensitive gene expression and chromatin accessibility patterns.

GFETM improves scATAC-seq modeling by integrating genome foundation model embeddings, revealing cell-state specific TF activity and kidney diabetes epigenomic signatures.

The paper introduces veloAgent, a framework that integrates spatial information with RNA velocity to improve accuracy and scalability in analyzing spatially resolved cellular dynamics.

CellSexID uses sex-linked gene expression to computationally track donor and recipient cells in chimeric models and beyond.

CellAgentChat uses agent-based modeling to analyze single-cell data and detect cell-cell interactions, offering visualization and perturbation capabilities.

Single-nucleus multiomic and ex vivo lung slice profiling of human idiopathic pulmonary fibrosis identifies Alveolar Intermediate cells bridging alveolar and Aberrant Basaloid states and reveals AP-1 chromatin injury memory.

DTractor improves cell-type deconvolution in spatial transcriptomics by integrating deep learning, transfer learning, and matrix factorization to better estimate cell-type proportions and gene expression with spatial coherence.

DTPSP selects optimal time points for time-series studies using deep learning, enabling efficient and targeted multi-omics profiling.

MATES improves locus-specific transposable element quantification in single-cell data by accurately allocating multi-mapping reads using contextual alignment information.

scHiCyclePred predicts cell cycle phases from single-cell Hi-C data using multi-scale interaction features, outperforming existing methods.

scCross integrates and generates single-cell multi-omics data using deep generative models, enabling seamless cross-modal analysis and in-silico experiments.

scSemiProfiler infers single-cell profiles from bulk data using deep generative models and active learning, validated across datasets to match true single-cell data.

scGeneRhythm clusters genes by time-frequency patterns in single-cell data using Fourier transformation and deep learning, outperforming traditional methods and revealing new biological insights.

scSniper identifies disease biomarkers using a deep neural network, outperforming traditional methods on real-world datasets.

DENetwork identifies regulatory and signaling networks behind differentially-expressed genes by integrating DE and non-DE genes in a graphical model, enhancing traditional differential gene analysis.

scCobra is a deep learning tool that integrates and harmonizes single-cell data by mitigating batch effects and enabling online label transfer.

UNIFAN improves cell type annotation in scRNA-Seq by simultaneously clustering and annotating cells using gene sets, outperforming prior methods.

The paper introduces TraSig, a method that uses pseudotime ordering from scRNA-Seq data to improve the inference of cell-cell interactions by identifying ligand-receptor pairs with similar trajectories.

Cellar is an interactive tool for annotating cell types in single-cell multi-omics and spatial proteomics data using HuBMAP datasets.

MESSI identifies active signaling genes in spatial single-cell data by using a mixture of experts approach, improving prediction accuracy and providing biological insights.

The paper reconstructs signaling and regulatory networks for SARS-CoV-2 infection and identifies potential drug targets by integrating multi-omics data and experimental validation.

The paper maps cell fate trajectories of human PSC-derived distal lung progenitors using scRNA-seq and identifies developmental plasticity regulated by Wnt signaling.

The paper develops a method to detect cell type-specific mutations from scRNA-Seq data and integrates them with expression data to improve lineage reconstruction.

The paper maps cardiac differentiation at single-cell resolution, identifies HOPX dysregulation, and shows hypertrophic signaling is required for HOPX transcription in hPSC-derived cardiomyocytes.