Zhongming Zhao

This study uses single-nucleus RNA-sequencing and multiome data to identify neuronal mitochondrial gene suppression as a driver of Alzheimer's dementia, revealing genetic and epigenetic mechanisms underlying cognitive resilience.

PerturbLDM predicts single-cell transcriptional responses to perturbations using a latent diffusion model, outperforming existing methods in drug-dose-cell line combinations and capturing key biological programs.

This paper identifies BMP signaling as a driver of heterotopic ossification by promoting ciliogenesis and establishing a sensitized signaling hub that facilitates osteochondrogenic differentiation.

This study shows combination immunotherapy enhances systemic antitumor immunity induced by serial radiofrequency ablation in pancreatic cancer by targeting CD73, PD-L1, and CSF1R.

This study shows that ERRγ activation in aged mice reduces sarcopenia by enhancing mitochondrial function, vascularization, and neuromuscular junctions while preserving exercise capacity.

Turep identifies tumor-reactive T cells in single-cell and spatial transcriptomics data across cancers, showing improved prediction of immunotherapy response.

This study creates a single-nucleus multiome atlas of sEOAD brain regions, identifying key transcription factors and regulatory elements linked to disease.

FADVI integrates single-cell and spatial omics data by disentangling batch effects from biological signals, outperforming existing methods across multiple datasets.

This study shows that ERRα promotes skeletal muscle regeneration and reduces dystrophy in DMD by enhancing angiogenesis and mitochondrial function.

GRPa-PRS identifies genetically-regulated pathways in polygenic diseases by stratifying individuals based on PRS and analyzing differential gene expression across risk strata.

CytoRescue recovers cytoplasmic gene expression in snRNA-seq data, improving quantification of cytoplasm-enriched genes while preserving expression signatures.

Scupa assesses immune cell polarization using a foundation model, identifying cytokine-driven states across species and conditions.

This study identifies 268 new viral transcriptional regulators (vTRs) from 14 families, maps their binding sites, and links them to immune-mediated diseases and cancers, suggesting therapeutic targets.

This paper introduces a model-agnostic method to discover gene-phenotype relationships and infer gene networks using in silico gene perturbation, showing improved prognostic performance over conventional biomarkers.

This study shows that genetic inactivation of beta-catenin improves desmoplakin cardiomyopathy by restoring oxidative phosphorylation gene expression, while its activation worsens the condition.

This study uses single-cell multi-omics to map regulatory programs during mouse secondary palate development, identifying key transcription factors involved in anterior and posterior palatal mesenchymal differentiation.

NetBCE is a deep learning model that accurately predicts linear B-cell epitopes using sequence features, outperforming existing tools with an AUC improvement of 22.06%.

This paper constructs a single-cell atlas of Alzheimer's disease brain tissue and identifies drug repositioning candidates by analyzing cellular heterogeneity and gene expression patterns.

deCS improves cell type annotation in scRNA-seq data by using a comprehensive human cell type reference atlas, enhancing accuracy and reducing computation time.

EmptyNN improves scRNA-seq quality by removing cell-free droplets and recovering lost cells using positive-unlabeled learning, achieving high AUROC scores and outperforming existing methods.

The paper identifies CXCR6 as a host genetic factor associated with severe COVID-19 through transcriptomic analysis and shows its protective effect in lung tissue.

INSCT integrates large scRNA-seq datasets across experiments, platforms, and species using a deep learning framework, outperforming existing methods in scalability and accuracy.

DrivAER identifies driving transcriptional programs in scRNA-seq data by scoring gene sets for their relevance to pseudotemporal ordering or disease status.

The paper uses single-cell RNA-seq to identify Krt8+ progenitor cells involved in lung regeneration and shows their role in epithelial differentiation and disease.

TSEA identifies tissue-specific genes and enriches query data by analyzing multi-trait genome-wide association and cancer expression data.

This paper introduces Gene2Vec, a gene embedding method using co-expression data from GEO databases, showing improved pathway recovery and gene interaction prediction.

scRNASeqDB is a database that compiles human single-cell RNA-seq datasets for gene expression analysis and provides tools for querying and visualizing expression profiles.